Note that when you do this with an online source, you should still include an access date, as in the example.
When a source lacks a clearly identified author, there’s often an appropriate corporate source – the organisation responsible for the source – whom you can credit as author instead, as in the Google and Wikipedia examples above.
When that’s not the case, you can just replace it with the title of the source in both the in-text citation and the reference list:
In-text citation | (‘Divest’, no date) |
Reference list entry | ‘Divest’ (no date) Available at: https://www.merriam-webster.com/dictionary/divest (Accessed: 27 January 2020). |
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Harvard referencing uses an author–date system. Sources are cited by the author’s last name and the publication year in brackets. Each Harvard in-text citation corresponds to an entry in the alphabetised reference list at the end of the paper.
Vancouver referencing uses a numerical system. Sources are cited by a number in parentheses or superscript. Each number corresponds to a full reference at the end of the paper.
Harvard style | Vancouver style | |
---|---|---|
In-text citation | Each referencing style has different rules (Pears and Shields, 2019). | Each referencing style has different rules (1). |
Reference list | Pears, R. and Shields, G. (2019). . 11th edn. London: MacMillan. | 1. Pears R, Shields G. Cite them right: The essential referencing guide. 11th ed. London: MacMillan; 2019. |
A Harvard in-text citation should appear in brackets every time you quote, paraphrase, or refer to information from a source.
The citation can appear immediately after the quotation or paraphrase, or at the end of the sentence. If you’re quoting, place the citation outside of the quotation marks but before any other punctuation like a comma or full stop.
In Harvard referencing, up to three author names are included in an in-text citation or reference list entry. When there are four or more authors, include only the first, followed by ‘ et al. ’
In-text citation | Reference list | |
---|---|---|
1 author | (Smith, 2014) | Smith, T. (2014) … |
2 authors | (Smith and Jones, 2014) | Smith, T. and Jones, F. (2014) … |
3 authors | (Smith, Jones and Davies, 2014) | Smith, T., Jones, F. and Davies, S. (2014) … |
4+ authors | (Smith , 2014) | Smith, T. (2014) … |
Though the terms are sometimes used interchangeably, there is a difference in meaning:
If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.
Caulfield, J. (2023, September 15). A Quick Guide to Harvard Referencing | Citation Examples. Scribbr. Retrieved 24 June 2024, from https://www.scribbr.co.uk/referencing/harvard-style/
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Home » How to Cite Research Paper – All Formats and Examples
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Research paper citation refers to the act of acknowledging and referencing a previously published work in a scholarly or academic paper . When citing sources, researchers provide information that allows readers to locate the original source, validate the claims or arguments made in the paper, and give credit to the original author(s) for their work.
The citation may include the author’s name, title of the publication, year of publication, publisher, and other relevant details that allow readers to trace the source of the information. Proper citation is a crucial component of academic writing, as it helps to ensure accuracy, credibility, and transparency in research.
There are several formats that are used to cite a research paper. Follow the guide for the Citation of a Research Paper:
Last Name, First Name. Title of Book. Publisher, Year of Publication.
Example : Smith, John. The History of the World. Penguin Press, 2010.
Journal Article
Last Name, First Name. “Title of Article.” Title of Journal, vol. Volume Number, no. Issue Number, Year of Publication, pp. Page Numbers.
Example : Johnson, Emma. “The Effects of Climate Change on Agriculture.” Environmental Science Journal, vol. 10, no. 2, 2019, pp. 45-59.
Research Paper
Last Name, First Name. “Title of Paper.” Conference Name, Location, Date of Conference.
Example : Garcia, Maria. “The Importance of Early Childhood Education.” International Conference on Education, Paris, 5-7 June 2018.
Author’s Last Name, First Name. “Title of Webpage.” Website Title, Publisher, Date of Publication, URL.
Example : Smith, John. “The Benefits of Exercise.” Healthline, Healthline Media, 1 March 2022, https://www.healthline.com/health/benefits-of-exercise.
News Article
Last Name, First Name. “Title of Article.” Name of Newspaper, Date of Publication, URL.
Example : Robinson, Sarah. “Biden Announces New Climate Change Policies.” The New York Times, 22 Jan. 2021, https://www.nytimes.com/2021/01/22/climate/biden-climate-change-policies.html.
Author, A. A. (Year of publication). Title of book. Publisher.
Example: Smith, J. (2010). The History of the World. Penguin Press.
Author, A. A., Author, B. B., & Author, C. C. (Year of publication). Title of article. Title of Journal, volume number(issue number), page range.
Example: Johnson, E., Smith, K., & Lee, M. (2019). The Effects of Climate Change on Agriculture. Environmental Science Journal, 10(2), 45-59.
Author, A. A. (Year of publication). Title of paper. In Editor First Initial. Last Name (Ed.), Title of Conference Proceedings (page numbers). Publisher.
Example: Garcia, M. (2018). The Importance of Early Childhood Education. In J. Smith (Ed.), Proceedings from the International Conference on Education (pp. 60-75). Springer.
Author, A. A. (Year, Month Day of publication). Title of webpage. Website name. URL
Example: Smith, J. (2022, March 1). The Benefits of Exercise. Healthline. https://www.healthline.com/health/benefits-of-exercise
Author, A. A. (Year, Month Day of publication). Title of article. Newspaper name. URL.
Example: Robinson, S. (2021, January 22). Biden Announces New Climate Change Policies. The New York Times. https://www.nytimes.com/2021/01/22/climate/biden-climate-change-policies.html
Please note that there are two main variations of the Chicago style: the author-date system and the notes and bibliography system. I will provide examples for both systems below.
Author-Date system:
Notes and Bibliography system:
JOURNAL ARTICLES:
RESEARCH PAPERS:
NEWS ARTICLES:
Format: Author’s Last name, First initial. (Year of publication). Title of book. Publisher.
Example: Smith, J. (2008). The Art of War. Random House.
Journal article:
Format: Author’s Last name, First initial. (Year of publication). Title of article. Title of journal, volume number(issue number), page range.
Example: Brown, M. (2012). The impact of social media on business communication. Harvard Business Review, 90(12), 85-92.
Research paper:
Format: Author’s Last name, First initial. (Year of publication). Title of paper. In Editor’s First initial. Last name (Ed.), Title of book (page range). Publisher.
Example: Johnson, R. (2015). The effects of climate change on agriculture. In S. Lee (Ed.), Climate Change and Sustainable Development (pp. 45-62). Springer.
Format: Author’s Last name, First initial. (Year, Month Day of publication). Title of page. Website name. URL.
Example: Smith, J. (2017, May 23). The history of the internet. Encyclopedia Britannica. https://www.britannica.com/topic/history-of-the-internet
News article:
Format: Author’s Last name, First initial. (Year, Month Day of publication). Title of article. Title of newspaper, page number (if applicable).
Example: Thompson, E. (2022, January 5). New study finds coffee may lower risk of dementia. The New York Times, A1.
Author(s). (Year of Publication). Title of Book. Publisher.
Smith, J. K. (2015). The Power of Habit: Why We Do What We Do in Life and Business. Random House.
Journal Article:
Author(s). (Year of Publication). Title of Article. Title of Journal, Volume Number (Issue Number), page numbers.
Johnson, T. J., & Kaye, B. K. (2016). Interactivity and the Future of Journalism. Journalism Studies, 17(2), 228-246.
Author(s). (Year of Publication). Title of Paper. Paper presented at Conference Name, Location.
Jones, L. K., & Brown, M. A. (2018). The Role of Social Media in Political Campaigns. Paper presented at the 2018 International Conference on Social Media and Society, Copenhagen, Denmark.
Example: National Aeronautics and Space Administration. (2019, August 29). NASA’s Mission to Mars. NASA. https://www.nasa.gov/topics/journeytomars/index.html
Example: Johnson, M. (2022, February 16). Climate Change: Is it Too Late to Save the Planet? CNN. https://www.cnn.com/2022/02/16/world/climate-change-planet-scn/index.html
In-text citation: Use superscript numbers to cite sources in the text, e.g., “The study conducted by Smith and Johnson^1 found that…”.
Reference list citation: Format: Author(s). Title of book. Edition if any. Place of publication: Publisher; Year of publication.
Example: Smith J, Johnson L. Introduction to Molecular Biology. 2nd ed. New York: Wiley-Blackwell; 2015.
In-text citation: Use superscript numbers to cite sources in the text, e.g., “Several studies have reported that^1,2,3…”.
Reference list citation: Format: Author(s). Title of article. Abbreviated name of journal. Year of publication; Volume number (Issue number): Page range.
Example: Jones S, Patel K, Smith J. The effects of exercise on cardiovascular health. J Cardiol. 2018; 25(2): 78-84.
In-text citation: Use superscript numbers to cite sources in the text, e.g., “Previous research has shown that^1,2,3…”.
Reference list citation: Format: Author(s). Title of paper. In: Editor(s). Title of the conference proceedings. Place of publication: Publisher; Year of publication. Page range.
Example: Johnson L, Smith J. The role of stem cells in tissue regeneration. In: Patel S, ed. Proceedings of the 5th International Conference on Regenerative Medicine. London: Academic Press; 2016. p. 68-73.
In-text citation: Use superscript numbers to cite sources in the text, e.g., “According to the World Health Organization^1…”.
Reference list citation: Format: Author(s). Title of webpage. Name of website. URL [Accessed Date].
Example: World Health Organization. Coronavirus disease (COVID-19) advice for the public. World Health Organization. https://www.who.int/emergencies/disease/novel-coronavirus-2019/advice-for-public [Accessed 3 March 2023].
In-text citation: Use superscript numbers to cite sources in the text, e.g., “According to the New York Times^1…”.
Reference list citation: Format: Author(s). Title of article. Name of newspaper. Year Month Day; Section (if any): Page number.
Example: Jones S. Study shows that sleep is essential for good health. The New York Times. 2022 Jan 12; Health: A8.
Author(s). Title of Book. Edition Number (if it is not the first edition). Publisher: Place of publication, Year of publication.
Example: Smith, J. Chemistry of Natural Products. 3rd ed.; CRC Press: Boca Raton, FL, 2015.
Journal articles:
Author(s). Article Title. Journal Name Year, Volume, Inclusive Pagination.
Example: Garcia, A. M.; Jones, B. A.; Smith, J. R. Selective Synthesis of Alkenes from Alkynes via Catalytic Hydrogenation. J. Am. Chem. Soc. 2019, 141, 10754-10759.
Research papers:
Author(s). Title of Paper. Journal Name Year, Volume, Inclusive Pagination.
Example: Brown, H. D.; Jackson, C. D.; Patel, S. D. A New Approach to Photovoltaic Solar Cells. J. Mater. Chem. 2018, 26, 134-142.
Author(s) (if available). Title of Webpage. Name of Website. URL (accessed Month Day, Year).
Example: National Institutes of Health. Heart Disease and Stroke. National Heart, Lung, and Blood Institute. https://www.nhlbi.nih.gov/health-topics/heart-disease-and-stroke (accessed April 7, 2023).
News articles:
Author(s). Title of Article. Name of News Publication. Date of Publication. URL (accessed Month Day, Year).
Example: Friedman, T. L. The World is Flat. New York Times. April 7, 2023. https://www.nytimes.com/2023/04/07/opinion/world-flat-globalization.html (accessed April 7, 2023).
In AMA Style Format, the citation for a book should include the following information, in this order:
Lodish H, Berk A, Zipursky SL, et al. Molecular Cell Biology. 4th ed. New York, NY: W. H. Freeman; 2000.
In AMA Style Format, the citation for a journal article should include the following information, in this order:
Chen H, Huang Y, Li Y, et al. Effects of mindfulness-based stress reduction on depression in adolescents and young adults: a systematic review and meta-analysis. JAMA Netw Open. 2020;3(6):e207081. doi:10.1001/jamanetworkopen.2020.7081
In AMA Style Format, the citation for a research paper should include the following information, in this order:
Bredenoord AL, Kroes HY, Cuppen E, Parker M, van Delden JJ. Disclosure of individual genetic data to research participants: the debate reconsidered. Trends Genet. 2011;27(2):41-47. doi:10.1016/j.tig.2010.11.004
In AMA Style Format, the citation for a website should include the following information, in this order:
Centers for Disease Control and Prevention. How to protect yourself and others. CDC. Published February 11, 2022. Accessed February 14, 2022. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html
In AMA Style Format, the citation for a news article should include the following information, in this order:
Gorman J. Scientists use stem cells from frogs to build first living robots. The New York Times. January 13, 2020. Accessed January 14, 2020. https://www.nytimes.com/2020/01/13/science/living-robots-xenobots.html
One author: Daniel J. Solove, The Future of Reputation: Gossip, Rumor, and Privacy on the Internet (Yale University Press 2007).
Two or more authors: Martha Nussbaum and Saul Levmore, eds., The Offensive Internet: Speech, Privacy, and Reputation (Harvard University Press 2010).
Journal article
One author: Daniel J. Solove, “A Taxonomy of Privacy,” University of Pennsylvania Law Review 154, no. 3 (January 2006): 477-560.
Two or more authors: Ethan Katsh and Andrea Schneider, “The Emergence of Online Dispute Resolution,” Journal of Dispute Resolution 2003, no. 1 (2003): 7-19.
One author: Daniel J. Solove, “A Taxonomy of Privacy,” GWU Law School Public Law Research Paper No. 113, 2005.
Two or more authors: Ethan Katsh and Andrea Schneider, “The Emergence of Online Dispute Resolution,” Cyberlaw Research Paper Series Paper No. 00-5, 2000.
WebsiteElectronic Frontier Foundation, “Surveillance Self-Defense,” accessed April 8, 2023, https://ssd.eff.org/.
News article
One author: Mark Sherman, “Court Deals Major Blow to Net Neutrality Rules,” ABC News, January 14, 2014, https://abcnews.go.com/Politics/wireStory/court-deals-major-blow-net-neutrality-rules-21586820.
Two or more authors: Siobhan Hughes and Brent Kendall, “AT&T Wins Approval to Buy Time Warner,” Wall Street Journal, June 12, 2018, https://www.wsj.com/articles/at-t-wins-approval-to-buy-time-warner-1528847249.
In-Text Citation: (Author’s last name Year of Publication: Page Number)
Example: (Smith 2010: 35)
Reference List Citation: Author’s last name First Initial. Title of Book. Edition. Place of publication: Publisher; Year of publication.
Example: Smith J. Biology: A Textbook. 2nd ed. New York: Oxford University Press; 2010.
Example: (Johnson 2014: 27)
Reference List Citation: Author’s last name First Initial. Title of Article. Abbreviated Title of Journal. Year of publication;Volume(Issue):Page Numbers.
Example: Johnson S. The role of dopamine in addiction. J Neurosci. 2014;34(8): 2262-2272.
Example: (Brown 2018: 10)
Reference List Citation: Author’s last name First Initial. Title of Paper. Paper presented at: Name of Conference; Date of Conference; Place of Conference.
Example: Brown R. The impact of social media on mental health. Paper presented at: Annual Meeting of the American Psychological Association; August 2018; San Francisco, CA.
Example: (World Health Organization 2020: para. 2)
Reference List Citation: Author’s last name First Initial. Title of Webpage. Name of Website. URL. Published date. Accessed date.
Example: World Health Organization. Coronavirus disease (COVID-19) pandemic. WHO website. https://www.who.int/emergencies/disease-coronavirus-2019. Updated August 17, 2020. Accessed September 5, 2021.
Example: (Smith 2019: para. 5)
Reference List Citation: Author’s last name First Initial. Title of Article. Title of Newspaper or Magazine. Year of publication; Month Day:Page Numbers.
Example: Smith K. New study finds link between exercise and mental health. The New York Times. 2019;May 20: A6.
The purpose of citing sources in a research paper is to give credit to the original authors and acknowledge their contribution to your work. By citing sources, you are also demonstrating the validity and reliability of your research by showing that you have consulted credible and authoritative sources. Citations help readers to locate the original sources that you have referenced and to verify the accuracy and credibility of your research. Additionally, citing sources is important for avoiding plagiarism, which is the act of presenting someone else’s work as your own. Proper citation also shows that you have conducted a thorough literature review and have used the existing research to inform your own work. Overall, citing sources is an essential aspect of academic writing and is necessary for building credibility, demonstrating research skills, and avoiding plagiarism.
There are several advantages of research paper citation, including:
Researcher, Academic Writer, Web developer
Last Updated: March 29, 2024 Fact Checked
This article was reviewed by Gerald Posner and by wikiHow staff writer, Jennifer Mueller, JD . Gerald Posner is an Author & Journalist based in Miami, Florida. With over 35 years of experience, he specializes in investigative journalism, nonfiction books, and editorials. He holds a law degree from UC College of the Law, San Francisco, and a BA in Political Science from the University of California-Berkeley. He’s the author of thirteen books, including several New York Times bestsellers, the winner of the Florida Book Award for General Nonfiction, and has been a finalist for the Pulitzer Prize in History. He was also shortlisted for the Best Business Book of 2020 by the Society for Advancing Business Editing and Writing. There are 8 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 417,300 times.
When writing a paper for a research project, you may need to cite a research paper you used as a reference. The basic information included in your citation will be the same across all styles. However, the format in which that information is presented is somewhat different depending on whether you're using American Psychological Association (APA), Modern Language Association (MLA), Chicago, or American Medical Association (AMA) style.
To cite a paper APA style, start with the author's last name and first initial, and the year of publication. Then, list the title of the paper, where you found it, and the date that you accessed it. In a paper, use a parenthetical reference with the last name of the author and the publication year. For an MLA citation, list the author's last name and then first name and the title of the paper in quotations. Include where you accessed the paper and the date you retrieved it. In your paper, use a parenthetical reference with the author's last name and the page number. Keep reading for tips on Chicago and AMA citations and exceptions to the citation rules! Did this summary help you? Yes No
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As an academician, terms such as citations, references and bibliography might be a part of almost every work-related conversation in your daily life. However, many researchers, especially during the early stages of their academic career, may find it hard to differentiate between citations, references and bibliography in research papers and often find it confusing to implement their usage. If you are amongst them, this article will provide you with some respite. Let us start by first understanding the individual terms better.
Citation in research papers: A citation appears in the main text of the paper. It is a way of giving credit to the information that you have specifically mentioned in your research paper by leading the reader to the original source of information. You will need to use citation in research papers whenever you are using information to elaborate a particular concept in the paper, either in the introduction or discussion sections or as a way to support your research findings in the results section.
Reference in research papers: A reference is a detailed description of the source of information that you want to give credit to via a citation. The references in research papers are usually in the form of a list at the end of the paper. The essential difference between citations and references is that citations lead a reader to the source of information, while references provide the reader with detailed information regarding that particular source.
Bibliography in research papers:
A bibliography in research paper is a list of sources that appears at the end of a research paper or an article, and contains information that may or may not be directly mentioned in the research paper. The difference between reference and bibliography in research is that an individual source in the list of references can be linked to an in-text citation, while an individual source in the bibliography may not necessarily be linked to an in-text citation.
It’s understandable how these terms may often be used interchangeably as they are serve the same purpose – namely to give intellectual and creative credit to an original idea that is elaborated in depth in a research paper. One of the easiest ways to understand when to use an in-text citation in research papers, is to check whether the information is an ongoing work of research or if it has been proven to be a ‘fact’ through reproducibility. If the information is a proven fact, you need not specifically add the original source to the list of references but can instead choose to mention it in your bibliography. For instance, if you use a statement such as “The effects of global warming and climate changes on the deterioration of environment have been described in depth”, you need not use an in-text citation, but can choose to mention key sources in the bibliography section. An example of a citation in a research paper would be if you intend to elaborate on the impact of climate change in a particular population and/or a specific geographical location. In this case, you will need to add an in-text citation and mention the correct source in the list of references.
Citations | References | Bibliography | |
Purpose | To lead a reader toward a source of information included in the text | To elaborate on of a particular source of information cited in the research paper | To provide a list of all relevant sources of information on the research topic
|
Placement | In the main text | At the end of the text; necessarily linked to an in-text citation | At the end of the text; not necessarily linked to an in-text citation
|
Information | Minimal; denoting only the essential components of the source, such as numbering, names of the first and last authors, etc.
| Descriptive; gives complete details about a particular source that can be used to find and read the original paper if needed | Descriptive; gives all the information regarding a particular source for those who want to refer to it |
Now that you have understood the basic similarities and differences in these terms, you should also know that every journal follows a particular style and format for these elements. So when working out how to write citations and add references in research papers, be mindful of using the preferred style of your target journal before you submit your research document.
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Citing resources will also avoid plagiarism, by crediting to those who provided the research used to create a paper.
Include a citation whenever you can. If you are not sure whether or not to cite a source, cite it. You should reference and cite whenever you:
You do not have to cite your source if the information you use is common knowledge. For example, the first African American President of the U.S. is Barack Obama; however, if you aren’t sure if it is common knowledge or not, go ahead and cite it, just to be safe.
There are three main types of sources: primary, secondary and peer-reviewed.
Primary sources may be in their original form or digitized, or reprinted or reproduced in some form. They are first-hand accounts of an event or period in history, or original documents. Primary sources include:
Secondary sources are written about primary sources and are one or more steps away from the original source. They include discussions, comments and interpretations regarding the primary source or original material. Examples of secondary source materials are as follows:
Usually published as an article in a medical or professional publication, such as a journal, a peer-reviewed source undergoes multiple critiques by top scholars in a particular field. Peer-reviewed articles offer authoritative information of the highest quality that scholarly disciplines can provide. Peer-reviewed and scholarly articles have these characteristics:
When you cite data from another author’s work, explain all related aspects of the work clearly and concisely using your own words. Always provide a reference to the work directly following the information you have provided.
Most colleges and organizations use a variety of citation styles. The citation style often depends on the professor, so always check before beginning a paper. No matter what the style you use for citing your paper, the process is always the same:
Researchers and writers should understand some of the following styles:
The American Psychology Association – Use this style for education, psychology, sociology and other social sciences.
Doe, J. (1999). Causes of the Civil War . Ohio: Smith Books.
Resources :
Modern Language Association – Use this style for arts, literature and the humanities.
Doe, John: “Causes of the Civil War.” Smith.
American Medical Association or the National Library of Medicine for health, medicine and biological sciences.
Doe JD. Causes of the Civil War . Columbus, OH: Smith Books; 1999.
Doe, JD. Causes of the Civil War. Columbus (OH): Smith Books; 1999.
Students and researchers commonly use the Chicago Manual of Style guide, or Turabian, for most real-world subjects in magazines, books, newspapers and many other non-scholarly publications.
Doe, John. 1999. Causes of the Civil War . Columbus, Ohio:
Smith Books.
There are a variety of scientific style guides depending on the particular field, whether it be biology, chemistry, engineering.
John D. Doe. Causes of the Civil War. Columbus (OH): Smith Press: 1999.
More Citation Examples
The following resources provide more examples for formatting citations:
The following resources provide programs to help researchers create citations:
When using a citation program, always check for errors before inserting them into your reference or works cited page.
The Annotated Bibliography or Reference Section
The reference page is also called the annotated bibliography , and it should go at the end of the research paper. The purpose of annotated bibliographies is to link each source to one another in an orderly fashion.
Here are six key factors for writing an annotation:
Completed example:
For more guidelines on creating an annotated bibliography, see the Purdue OWL: Annotated Bibliographies site, which includes additional sample annotations .
Adding citations may seem difficult at first; however, the more you practice, the easier it will become for you. By using a style guide and checking examples, citing all your sources is simple and complete.
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When is citing necessary, citing textual sources, citing images and non-textual sources, citing generative ai, links to style guides and citation resources.
Reasons for citing sources are based on academic, professional, and cultural values. At the GSD, we cite to promote
Cite your source whenever you quote, summarize, paraphrase, or otherwise include someone else's
Citations follow different rules for structure and content depending on which style you use. At the GSD, mostly you will use Chicago or APA style. Often you can choose the style you prefer, but it's good to ask your professor or TA/TF. Whichever style you use, be consistent. We recommend using Zotero , a citation-management tool, to structure your citations for you, but you should always check to make sure the tool captures the correct information in the correct place.
Citing print sources.
Footnote - long (first time citing the source)
1. Joseph Rykwert, The Idea of a Town: The Anthropology of Urban Form in Rome, Italy and the Ancient World , (New Jersey: Princeton University Press, 1976), 35.
Footnote - short (citing the source again)
1. Rykwert, The Idea of a Town , 35.
In-text citation (alternative to footnotes)
(Rykwert 1976, 35)
Bibliography (alphabetical order and hanging indentation)
Rykwert, Joseph. The Idea of a Town: the Anthropology of Urban Form in Rome, Italy and the Ancient World . New Jersey: Princeton University Press, 1976.
1. Diane Favro, “The Street Triumphant: The Urban Impact of Roman Triumphal Parades,” in Streets: Critical Perspectives on Public Space , ed. Zeynep Çelik , Diana Favro, and Richard Ingersoll (Berkeley: University of California Press,1994), 153.
1. Favro, “The Street Triumphant,” 156.
In-text citation (called "author-date," an alternative to footnotes)
(Favro 1994, 153)
Bibliography (alphabetical order and hanging indentation)
Favro, Diane. “The Street Triumphant: The Urban Impact of Roman Triumphal Parades.” In Streets: Critical Perspectives on Public Space, edited by Zeynep Çelik, Diane G. Favro, and Richard Ingersoll, 151-164. Berkeley: University of California Press, 1994.
1. Hendrik Dey, “From ‘Street’ to ‘Piazza’: Urban Politics, Public Ceremony, and the Redefinition of platea in Communal Italy and Beyond” Speculum 91, no.4 (October 2016): 919.
1. Dey, “From ‘Street’ to ‘Piazza,’” 932.
Dey, Hendrik. “From ‘Street’ to ‘Piazza’: Urban Politics, Public Ceremony, and the Redefinition of platea in Communal Italy and Beyond.” Speculum 91, no.4 (October 2016): 919-44.
In-text citation
(Rykwert 1976 p. 35)
Footnote (for supplemental information)
1. From The idea of a town: The anthropology of urban form in Rome, Italy and the ancient world by Joseph Rykwert, 1976, New Jersey: Princeton University Press.
Bibliography/Reference (alphabetical order and hanging indentation)
Rykwert, J. (1976). The idea of a town: The anthropology of urban form in Rome, Italy and the ancient world . Princeton University Press.
In-Text Citation
(Favro 1994 p.153)
Footnote (for supplemental information)
1. From the chapter "The street triumphant: The urban impact of Roman triumphal parades" in Streets: Critical perspectives on public space, edited by Zeynep Çelik , Diana Favro, and Richard Ingersoll, 1994, Berkeley: University of California Press.
Favro, D. (1994) “The street triumphant: The Urban Impact of Roman Triumphal Parades.” In Zeynep Çelik, Diane G. Favro, and Richard Ingersoll (Eds.), Streets: Critical Perspectives on Public Space ( pp.151-164). University of California Press.
(Dey 2016 p.919)
Footnote (for supplemental material)
1. From the article “From ‘street’ to ‘Piazza’: Urban politics, public ceremony, and the Redefinition of platea in Communal Italy and Beyond” by Hendrik Dey in Speculum 91(4), 919. www.journals.uchicago.edu/toc/spc/2016/91/4
Dey, H. (2016). From "street" to "piazza": Urban politics, public ceremony, and the redefinition of platea in communal Italy and beyond. Speculum 91 (4), 919-44. www.journals.uchicago.edu/toc/spc/2016/91/4
Visual representations created by other people, including photographs, maps, drawings, models, graphs, tables, and blueprints, must be cited. Citations for visual material may be included at the end of a caption or in a list of figures, similar to but usually separate from the main bibliography.
When they are not merely background design, images are labeled as figures and numbered. In-text references to them refer to the figure number. Sometimes you will have a title after the figure number and a brief descriptive caption below it.
If you choose to include the citation under the caption, format it like a footnote entry. If you would prefer to have a list of figures for citation information, organize them by figure number and use the format of a bibliographic entry.
The construction of citations for artwork and illustrations is more flexible and variable than textual sources. Here we have provided an example with full bibliographic information. Use your best judgment and remember that the goals are to be consistent and to provide enough information to credit your source and for someone else to find your source.
Some borrowed material in collages may also need to be cited, but the rules are vague and hard to find. Check with your professor about course standards.
Visual representations created by other people, including photographs, maps, drawings, models, graphs, tables, and blueprints, must be cited. In APA style, tables are their own category, and all other visual representations are considered figures. Tables and figures both follow the same basic setup.
When they are not merely background design, images are labeled as figures and numbered and titled above the image. If needed to clarify the meaning or significance of the figure, a note may be placed below it. In-text references to visual sources refer to the figure number (ex. As shown in Figure 1...").
Citations for visual material created by other people may either be included under the figure or note or compiled in a list of figures, similar to but usually separate from the main bibliography.
Figures may take up a whole page or be placed at the top or bottom of the page with a blank double-space below or above it.
If you choose to include the citation under the figure, format it like a bibliographic entry. If you would prefer to have a list of figures for citation information, organize them by figure number and use the format of a bibliographic entry. Here is a detailed example. Some figures will require less bibliographic information, but it is a good practice to include as much as you can.
The construction of citations for artwork and illustrations is more flexible and variable than for textual sources. Here we have provided an example with full bibliographic information. Use your best judgment and remember that the goals are to be consistent and to provide enough information to credit your source and for someone else to find your source.
The rules for citing the use of generative AI, both textual and visual, are still evolving. For guidelines on when to cite the use of AI, please refer to the section on Academic Integrity. Here, we will give you suggestions for how to cite based on what the style guides say and what Harvard University encourages. We again recommend that you to ask your instructors about their expectations for use and citation and to remain consistent in your formatting.
The Chicago Manual of Style currently states that "for most types of writing, you can simply acknowledge the AI tool in your text" with a parenthetical comment stating the use of a specific tool. For example: (Image generated by Midjourney).
For academic papers or research articles, you should have a numbered footnote or endnote
Footnote - prompt not included in the text of the paper
1. ChatGPT, response to "Suggest three possible responses from community stakeholders to the proposed multi-use development project," OpenAI, March 28, 2024, https://chat.openai.com/chat.
Footnote - prompt included in the text of the paper
1. Text generated by ChatGPT, OpenAI, March 28, 2024, https://chat.oenai.com/chat
Footnote - edited AI-generated text
1. Text generated by ChatGPT, OpenAI, March 28, 2024, edited for clarity, https://chat.oenai.com/chat
In-text citation (called "author-date," an alternative to footnotes)
(Text generated by ChatGPT, OpenAI) or (Text generated by ChatGPT, OpenAI, edited for clarity)
Chicago does not encourage including generative AI in a bibliography unless the tool also generates a direct link to the same generated content.
https://www-chicagomanualofstyle-org.ezp-prod1.hul.harvard.edu/qanda/data/faq/topics/Documentation/faq0422.html
The APA style team currently says to "describe how you used the tool in your Methods section or in a comparable section of your paper," perhaps the introduction for literature reviews and response papers. In your paper, state the prompt followed by the resulting generated text. Cite generative AI use according to the rules you would use for citing an algorithm. Include the URL if it leads directly to the same generated material; otherwise, the URL is optional.
(OpenAI, 2024)
Footnote (for supplemental material)
APA does not yet provide a structure or example for a footnote. If you need to mention generative AI in a footnote, stay as consistent with formatting as possible.
OpenAI. (2024). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat
These links take you to external resources for further research on citation styles.
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Considering credibility, familiarity, and patience when citing research..
Posted June 12, 2024 | Reviewed by Davia Sills
I came across an interesting social media post recently in preparation for a professional skills development workshop that I was presenting. The post discussed how academics tend to “cite up” in terms of referencing older, more famous scholars relative to more junior researchers. I thought about this proposition in light of my own citation strategies and knowledge of bibliometrics and concluded that this statement is likely true, but probably not for any explicit bias against junior researchers, as some might posit.
First and foremost, we must consider the purpose of citing research—to represent a source of evidence and indicate that someone didn’t just make up what they’re saying. It’s been established in previous work, and we pay that research kudos to further our argument in context. References are also useful for “ cutting a long story short”—one can cite another’s work that can more fully explain a concept without having to reiterate the whole thing. When I use a reference in my arguments, given that I’m trying to convince the reader of my point, I want to use the most credible source(s) that I can find.
If Author A is at the apex of credible sources in the field, I’m going to cite them where appropriate. Indeed, if I was reviewing a relevant paper and didn’t see Author A cited, I might be concerned. Of course, one can include multiple citations, but perhaps the reason why more junior or early career researchers are not cited (relative to the Author As out there) is that other researchers may not be as familiar with the early career researchers”—Author Es’—research.
Maybe the citing researcher remembers the research but not the name of the author. Obviously, Author E’s work hasn’t seemed to “stick” yet, maybe because they’re yet to make a bigger impact in the field. Sure, that’s largely the citing researcher’s issue for not having better organized their reading and note-taking, but simply, it’s also an issue of accessibility. If a researcher can’t remember Author E’s name in this context, the credibility of Author A will more than suffice. “Citing up” is not a slight here; it’s just that Author E’s contribution might not be that impactful, accessible, or memorable to a more established researcher. Moreover, I must admit there might be a level of laziness here.
For example, the scenario above is context-dependent. If I can’t remember Author E, that’s fine; I have Author A to cite. However, if Author E is the only appropriate citation, the citing strategy will change. If I know a claim is fundamental to my rationale but I can’t remember where it came from, despite knowing I’ve seen solid evidence for it in the past, I will search for Author E’s paper until I find it (because I have to if there’s no Author A to rely on). This might take time and effort.
I can imagine that some researchers will be reading this and thinking, “Surely, others are reading the new literature and taking notes as they go along or maybe even writing the rationale as they engage the new literature.” Ideally, this should be the case; indeed, it’s a handy way of keeping up-to-date with the literature. However, this does not always happen.
I imagine more established researchers in a field are “familiar enough” with it to write a rationale without having to look up papers every few lines and, instead, are more likely to write what they know. Such is human nature. When they eventually get some free time, they might dedicate a few hours to reading recently published papers. I’m also aware that some researchers are better at this than others. Obviously, this is worrying in the realm of research—perhaps more worrying altogether than the issue of “citing-up.”
With that, what are the chances that a researcher has read every paper in their field? Slim-to-none. Given the exponential increase in the amount of information available to people in the past 25 years and, likewise, the increase in the amount of Ph.D. degrees awarded and research being conducted, being up-to-date with all work in a field just isn’t feasible.
So, maybe “lazy” is unfair in context. Maybe these researchers are indeed reading as much as they can, but because the amount that’s feasible is finite relative to the seemingly endless new research that’s coming out, they might be “pickier” in what they read; for example, prioritizing known and credible researchers in their field. So, there’s a good chance that when only Author A is cited regarding a particular finding, it’s quite possible that it’s because the citing researcher has never even heard of Author E’s paper, let alone read it.
“New” papers—regardless of when and by whom they’re read, need “sticking power,” and by that, I mean that the research is well-conducted: It is well-written, and interesting food for thought is provided. I compile and read new papers every month—maybe one per session has any sticking power—and that’s not because I’m some kind of research snob; rather, it’s the case that much of it failed some of the criteria above. With that, if the paper had well-conducted research, was well-written, and provided either something novel or some food for thought, then regardless of familiarity, this paper (and its author) would be on my radar for the future. So, just as much as older researchers may be set in their bibliographies or “lazy” referencing, it is most definitely up to younger researchers to publish impactful work.
I completely understand how this is frustrating for early career researchers. I was there once, too. Even though it’s been well over 10 years since I received my Ph.D., I still find myself trying to make the aforementioned impact necessary to be considered one of those “A” researchers in the field. Of course, I get annoyed when I see missed opportunities for other researchers to cite my work. But I’m realistic enough to recognize that maybe they have not come across my work, I have not made a large enough impact for it to be noticed, or the research they did cite was sufficient to make their point. I don’t take it personally, and neither should young researchers. Their time will come, but they must be patient.
Consider the research by Morris, Wooding, and Grant (2011), where it was suggested that it takes approximately 17 years on average for health research implementation from “bench to bedside.” That’s a long time for “research to be realized.” I know citations are different and should be more visible quicker in the land of research, but the same logic applies. Patience—and continued hard work (i.e., to advance one’s research acumen)—are necessary for citation success.
Again, I don’t think that “citing up” is consciously done to slight early career academics; researchers are not conspiring against their junior colleagues—at least, not in my field. If anything, they want to see them and their field flourish. Instead, I think it’s more likely that this issue boils down to an implicit bias (which we all face on a day-to-day basis) toward what we know as familiar, accessible, and credible.
Morris, Z. S., Wooding, S., & Grant, J. (2011). The answer is 17 years, what is the question: understanding time lags in translational research. Journal of the royal society of medicine , 104 (12), 510-520.
Christopher Dwyer, Ph.D., is a lecturer at the Technological University of the Shannon in Athlone, Ireland.
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As the volume of scientific literature expands rapidly, accurately gauging and predicting the citation impact of academic papers has become increasingly imperative. Citation counts serve as a widely adopted metric for this purpose. While numerous researchers have explored techniques for projecting papers’ citation counts, a prevalent constraint lies in the utilization of a singular model across all papers within a dataset. This universal approach, suitable for small, homogeneous collections, proves less effective for large, heterogeneous collections spanning various research domains, thereby curtailing the practical utility of these methodologies. In this study, we propose a pioneering methodology that deploys multiple models tailored to distinct research domains and integrates early citation data. Our approach encompasses instance-based learning techniques to categorize papers into different research domains and distinct prediction models trained on early citation counts for papers within each domain. We assessed our methodology using two extensive datasets sourced from DBLP and arXiv. Our experimental findings affirm that the proposed classification methodology is both precise and efficient in classifying papers into research domains. Furthermore, the proposed prediction methodology, harnessing multiple domain-specific models and early citations, surpasses four state-of-the-art baseline methods in most instances, substantially enhancing the accuracy of citation impact predictions for diverse collections of academic papers.
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The rapid advancement of science and technology has led to a staggering increase in the number of academic publications produced globally each year (Zhu & Ban, 2018 ). In this ever-growing landscape, effectively evaluating the impact of research papers has become a critical issue (Castillo et al., 2007 ; Chakraborty et al., 2014 ; Li et al., 2019 ; Yan et al., 2011 ). Citation count, which measures the frequency with which a paper is referenced by other works, is widely recognized as the most prevalent metric for assessing the influence of academic papers, authors, and institutions (Bu et al., 2021 ; Cao et al., 2016 ; Lu et al., 2017 ; Redner, 1998 ; Stegehuis et al., 2015 ; Wang et al., 2021 ). Building upon the foundation of citation counts, numerous additional measures have been proposed to quantify research impact from various perspectives (Braun et al., 2006 ; Egghe, 2006 ; Garfield, 1972 , 2006 ; Hirsch, 2005 ; Persht, 2009 ; Yan & Ding, 2010 ).
Predicting the impact of scientific papers has garnered significant research attention due to its profound implications (Abramo et al., 2019 ; Abrishami & Aliakbary, 2019 ; Bai et al., 2019 ; Cao et al., 2016 ; Chen & Zhang, 2015 ; Li et al., 2019 ; Liu et al., 2020 ; Ma et al., 2021 ; Ruan et al., 2020 ; Su, 2020 ; Wang et al., 2013 , 2021 , 2023 ; Wen et al., 2020 ; Xu et al., 2019 ; Yan et al., 2011 ; Yu et al., 2014 ; Zhao & Feng, 2022 ; Zhu & Ban, 2018 ). See “ Citation count prediction ” section for more detailed discussion about them. Accurately forecasting the future citation impact of academic papers, particularly those recently published, offers invaluable benefits to various stakeholders within the research ecosystem. Precisely predicting the impact of papers, especially those published for a short time, would be helpful for researchers to find potentially high-impact papers and interesting research topics at an earlier stage. It is also helpful for institutions, government agencies, and funding bodies to evaluate published papers, researchers, and project proposals, among others.
For large and diverse collections encompassing papers from various research areas, a one-size-fits-all approach to citation impact prediction may be inadequate. Even within a broad field like Computing, sub-fields such as Theoretical Computing, Artificial Intelligence, Systems, and Applications can exhibit distinct citation patterns. Previous study has demonstrated that citation dynamics can vary significantly across research areas, journals, researchers in different age groups, among other factors (Kelly, 2015 ; Levitt & Thelwall, 2008 ; Mendoza, 2021 ; Milz & Seifert, 2018 ). To illustrate this point, let us consider an example from the DBLP dataset used in our study. Figure 1 a depicts the average citation distributions of papers in three research areas: Cryptography, Computer Networks, and Software Engineering. We can observe striking differences in their citation patterns:
Software Engineering papers consistently attract relatively few citations over time, without a pronounced peak in their citation curve.
Artificial Intelligence papers garner the highest citation counts among the three areas. Their citation curve rises rapidly, peaking around year 4, followed by a gradual decline until year 7, after which the decrease becomes more precipitous.
Cryptography papers exhibit a steadily increasing citation trend over the first 10 years, reaching a peak around year 11, followed by a slow decline in citations thereafter.
Citation patterns in different research areas or different classes of the same research area
These divergent citation patterns across research areas highlight the limitations of employing a single, universal model for citation impact prediction. In light of these observations, a more effective strategy would be to segment papers into distinct groups based on their research areas and develop tailored prediction models for each group. By accounting for the unique citation characteristics of different domains, such a group-specific modelling approach has the potential to significantly enhance the accuracy and reliability of citation impact predictions, particularly for large and heterogeneous collections of academic papers.
Citation patterns are not solely determined by research areas but also influenced by the quality and intrinsic characteristics of individual papers. Even within the same research area, the citation dynamics of papers can vary considerably (Garfield, 2006 ; Wang et al., 2021 ; Yan & Ding, 2010 ). High-impact papers may exhibit significantly different citation trajectories compared to average or low-impact works. Accounting for these differences by employing multiple models tailored to papers with varying citation potential could further improve prediction performance. Figure 1 b illustrates this phenomenon using an example from the Embedded & Real-Time Systems research area. All papers in this domain can be categorized into four classes based on their cumulative citation counts ( cc ) over 15 years: cc < 10, 10 ≤ cc < 50, 50 ≤ cc < 100, and cc ≥ 100. The general pattern observed for all the curves is that they initially increase for a few years and then decrease afterwards. However, the peak point varies depending on the total number of citations. Papers with higher citation counts take more years to reach their peak point. This finding suggests that class-based prediction can be a viable approach for our prediction task, as it account for the varying peak times based on the citation count classes.
If all of the papers are not classified, then it is necessary to have a classification system that encompasses multiple categories and an automated method for allocating each paper into one or more suitable categories. For a large collection of papers to be classified, both the effectiveness and efficiency of the allocating method are crucial factors to consider.
Taking into account all the observations mentioned earlier, we propose MM, a prediction method based on Multiple Models tailored for different research areas and citation counts, to predict the future citation counts of a paper. This work makes the following contributions:
A new instance-based learning method is introduced to classify papers into a given number of research areas. Both paper contents (titles and abstracts) and citations are considered separately. An ensemble-based method is then employed to make the final decision. Experiments with the DBLP dataset demonstrate that the proposed method can achieve excellent classification performance.
A prediction method for paper citation counts is proposed. For any paper to be predicted, a suitable prediction model is chosen based on its research area and early citation history. This customized approach enables each document to use a fitting model.
Experiments with two datasets show that the proposed prediction method outperforms four baseline methods in this study, demonstrating its superiority.
The remainder of this article is structured as follows: “ Related work ” section reviews related work on citation count prediction and classification of academic papers. “ Methodology ” section describes the proposed method in detail. “ Experimental settings and results ” section presents the experimental settings, procedures, and results, along with an analysis of the findings. Finally, “ Conclusion ” section concludes the paper.
In this work, the primary task is citation count prediction of papers, while classification of scientific papers serves as an additional task that may be required for the prediction task. Accordingly, we review some related work on citation count prediction and classification of academic papers separately in the following sections.
In the literature, there are numerous papers on predicting the citation counts of scientific papers. These methods can be categorized into three groups based on the information used for prediction.
The first group relies solely on the paper’s citation history as input. Wang et al. ( 2013 ) developed a model called WSB to predict the total number of citations a paper will receive, assuming its earlier citation data is known. Cao et al. ( 2016 ) proposed a data analytic approach to predict the long-term citation count of a paper using its short-term (three years after publication) citation data. Given a large collection of papers C with long citation histories, for a paper p with a short citation history, they matched it with a group of papers in C with similar early citation data and then used those papers in C to predict p’s later citation counts. Abrishami and Aliakbary ( 2019 ) proposed a long-term citation prediction method called NNCP based on Recurrent Neural Network (RNN) and the sequence-to-sequence model. Their dataset comprised papers published in five authoritative journals: Nature, Science, NEJM (The New England Journal of Medicine), Cell, and PNAS (Proceedings of the National Academy of Sciences). Wang et al. ( 2021 ) introduced a nonlinear predictive combination model, NCFCM, that utilized multilayer perceptron (MLP) to combine WSB and an improved version of AVR for predicting citation counts.
The second group uses not only the citation data but also some other extracted features from the paper or the wider academic network for the prediction task. Yu et al. ( 2014 ) adopted a stepwise multiple regression model using four groups of 24 features, including paper, author, publication, and citation-related features. Bornmann et al. ( 2014 ) took the percentile approach of Hazen ( 1914 ), considering the journal’s impact and other variables such as the number of authors, cited references, and pages. Castillo et al. ( 2007 ) used information about past papers written by the same author(s). Chen and Zhang ( 2015 ) applied Gradient Boosting Regression Trees (GBRT) with six paper content features and 10 author features. Bai et al. ( 2019 ) made long-term predictions using the Gradient Boosting Decision Tree (GBDT) model with five features, including the citation count within 5 years after publication, authors’ impact factor, h-index, Q value, and the journal's impact factor. Akella et al. ( 2021 ) exploited 21 features derived from social media shares, mentions, and reads of scientific papers to predict future citations with various machine learning models, such as Random Forest, Decision Tree, Gradient Boosting, and others. Xu et al. ( 2019 ) extracted 22 features from heterogeneous academic networks and employed a Convolutional Neural Network (CNN) to capture the complex nonlinear relationship between early network features and the final cumulative citation count. Ruan et al. ( 2020 ) employed a four-layer BP neural network to predict the 5th year citation counts of papers, using a total of 30 features, including paper, author, publication, reference, and early citation-related features. By extracting high-level semantic features from metadata text, Ma et al. ( 2021 ) adopted a neural network to consider both semantic information and the early citation counts to predict long-term citation counts. Wang et al. ( 2023 ) applied neural network technology to a heterogeneous network including author and paper information. Huang et al. ( 2022 ) argued that citations should not be treated equally, as the citing text and the section in which the citation occurs significantly impact its importance. Thus, they applied deep learning models to perform fine-grained citation prediction—not just citation count for the whole paper but citation count occurring in each section.
The third group uses other types of information beyond those mentioned above. To investigate the impact of peer-reviewing data on prediction performance, Li et al. ( 2019 ) adopted a neural network prediction model, incorporating an abstract-review match method and a cross-review match mechanism to learn deep features from peer-reviewing texts. Combining these learned features with breadth features (topic distribution, topic diversity, publication year, number of authors, and average author h-index), they employed a multilayer perceptron (MLP) to predict citation counts. Li et al. ( 2022 ) also utilized peer-reviewing text for prediction, using an aspect-aware capsule network. Zhao and Feng ( 2022 ) proposed an end-to-end deep learning framework called DeepCCP, which takes an early citation network as input and predicts the citation count using both GRU and CNN, instead of extracting features.
Citation counts of a paper can be affected by many factors such as research areas, paper types, age, sex, and other aspects of the authors (Andersen & Nielsen, 2018 ; Mendoza, 2021 ; Thelwall, 2020 ). Levitt and Thelwall ( 2008 ) compared patterns of annual citations of highly cited papers across six research areas. To our knowledge, Abramo et al. ( 2019 ) is the only work that uses multiple regression models for prediction, with one model for each subject category. Abramo et al. ( 2019 ) is the most relevant to our work in this article. However, there are two major differences. First, we propose a paper classification method in this paper, while no paper classification is required in Abramo et al. ( 2019 ). Second, we apply multiple models for papers in each category, whereas only one model is used for each category in Abramo et al. ( 2019 ).
Classification of scientific papers becomes a critical issue when organizing and managing an increasing number of publications through computerized solutions. In previous research, typically, meta-data such as title, abstract, keywords, and citations of papers were used for this task, while full text was not considered due to its unavailability in most situations.
Various machine learning methods, such as K-Nearest Neighbors (Lukasik et al., 2013 ; Waltman & Van Eck, 2012 ), K-means (Kim & Gil, 2019 ), and Naïve Bayes (Eykens et al., 2021 ), have been applied. Recently, deep neural network models, such as Convolutional Neural Networks (Daradkeh et al., 2022 ; Rivest et al., 2021 ), Recurrent Neural Networks (Hoppe et al., 2021 ; Semberecki & Maciejewski, 2017 ), and pre-trained language models (Hande et al., 2021 ; Kandimalla et al., 2020 ), have also been utilized.
One key issue is the classification system to be used. There are many different classification systems. Both Thomson Reuters’ Web of Science database (WoS) and Elsevier’s Scopus database have their own general classification systems, covering many subjects/research areas. Some systems focus on one particular subject, such as the medical subject headings (MeSH), the physics and astronomy classification scheme (PACS), the Chemical Abstracts Sections, the journal of economic literature (JEL), and the ACM Computing Classification System.
Based on the WoS classification system, Kandimalla et al. ( 2020 ) applied a deep attentive neural network (DANN) to a collection of papers from the WoS database for the classification task. It was assumed that each paper belonged to only one category, and only abstracts were used.
Zhang et al. ( 2022 ) compared three classification systems: Thomson Reuters’ Web of Science, Fields of Research provided by Dimensions, and the Subjects Classification provided by Springer Nature. Among these, the second one was generated by machine learning methods automatically, while the other two were generated manually by human experts. It is found there are significant inconsistency between machine and human-generated systems.
Rather than using an existing classification system, some researchers build their own classification system using the collection to be classified or other resources such as Wikipedia.
Shen et al. ( 2018 ) organized scientific publications into a hierarchical concept structure of up to six levels. The first two levels (similar to areas and sub-areas) were manually selected, while the others were automatically generated. Wikipedia pages were used to represent the concepts. Each publication or concept was represented as an embedding vector, thus the similarity between a publication and a concept could be calculated by the cosine similarity of their vector representations. It is a core component for the construction of the Microsoft Academic Graph.
In the same vein as Shen et al. ( 2018 ), Toney-Wails and Dunham ( 2022 ) also used Wikipedia pages to represent concepts and build the classification system. Both publications and concepts were represented as embedding vectors. Their database contains more than 184 million documents in English and more than 44 million documents in Chinese.
Mendoza et al. ( 2022 ) presented a benchmark corpus and a classification system as well, which could be used for the academic paper classification task. The classification system used is the 36 subjects defined in the UK Research Excellent Framework. Footnote 1 According to Cressey and Gibney ( 2014 ), this practice is the largest overall assessment of university research outputs ever undertaken globally. The 191,000 submissions to REF 2014 comprise a very good data set because every paper was manually categorized by experts when submitted.
Liu et al. ( 2022 ) described the NLPCC 2022 Task 5 Track 1, a multi-label classification task for scientific literature, where one paper may belong to multiple categories simultaneously. The data set, crawled from the American Chemistry Society’s publication website, comprises 95,000 papers’ meta-data including titles and abstracts. A hierarchical classification system, with a maximum of three levels, was also defined.
As we can see, the classification problem of academic papers is quite complicated. Many classification systems and classification methods are available. However, classification systems and classification methods are related to each other. The major goal of this work is to perform citation count prediction of published papers, in which classification of papers is a basic requirement. For example, considering the DBLP dataset which includes over four million papers, special consideration is required to perform the classification task effectively and efficiently. We used the classification system from CSRankings, Footnote 2 which included a set of four categories (research areas) and 26 sub-categories in total. A group of top venues were identified for each sub-category. However, many more venues in DBLP are not assigned to any category. We used all those recommended venue papers in the CSRankings system as representative papers of a given research area. An instance-based learning approach was used to measure the semantic similarity of the target paper and all the papers in a particular area. A decision could be made based on the similarity scores that the target paper obtained for all research areas. Besides, citation data between the target paper and all the papers in those recommended venues is also considered. Quite different from those proposed classification methods before, this instance-based learning approach suits our purpose well. See “ Methodology ” section for more details.
This research aims to predict the number of citations of academic papers in the next couple of years based on their metadata including title, abstract and citation data since publication. The main idea of our approach is: for a paper, depends on its research area and early citation count, we use a specific model to make the prediction. There are two key issues. Academic paper classification and citation count prediction methods. Let us detail them one by one in the following subsections.
To carry out the classification task of academic papers, a suitable classification system is required. There are many classification systems available for natural science, social science, humanities, or specific branches of science or technology. Since one of the datasets used in this study is DBLP, which includes over four million papers on computer science so far, we will focus our discussion on classification systems and methods for computer science.
In computer science, there are quite a few classification systems available. For example, both the Association for Computing Machinery (ACM) and the China Computer Federation (CCF) define their own classification systems. However, both are not very suitable for our purpose. The ACM’s classification system is quite complicated, but it does not provide any representative venues for any of the research areas. The CCF defines 10 categories and recommends dozens of venues in each category. However, some journals and conferences publish papers in more than one category, but they are only recommended in one category. For instance, both the journals IEEE Transactions on Knowledge and Data Engineering and Data and Knowledge Engineering publish papers on Information Systems and Artificial Intelligence, but they are only recommended in the Database/Data Mining/Content Retrieval category.
In this research, we used the classification system from CSRankings. This system divides computer science into four areas: AI, System, Theory, and Interdisciplinary Areas. Then, each area is further divided into several sub-areas, totalling 26 sub-areas. We flatten these 26 sub-areas for classification, while ignoring the four general areas at level one. One benefit of using this system is that it lists several key venues for every sub-area. For example, three venues are identified for Computer Vision: CVPR (IEEE Conference on Computer Vision and Pattern Recognition), ECCV (European Conference on Computer Vision), and ICCV (IEEE International Conference on Computer Vision). This is very useful for the paper classification task, as we will discuss now.
For this research, we need a classification algorithm that can perform the classification task for all the papers in the DBLP dataset effectively and efficiently.
Although many classification methods have been proposed, we could not find a method that suits our case well. Therefore, we developed our own approach. Using the classification system of CSRankings, we assume that all the papers published in those identified venues belong to that given research area, referred to as seed papers. For all the non-seed papers, we need to decide the areas to which they belong. This is done by considering three aspects together: content, references, and citations. Let us look at the first aspect first.
The collection of all the seed papers, denoted as C , was indexed using the Search engine Lucene Footnote 3 with the BM25 model. Both titles and abstracts were used in the indexing process. Each research area \({a}_{k}\) is presented by all its seed papers C ( \({a}_{k}\) ). For a given non-seed paper p , we use its title and abstract as a query to search for similar papers in C . Then each seed paper s will obtain a score (similarity between p and s )
in which b 1 and b 2 are two parameters (set to 0.75 and 1.2, respectively, as default setting values of Lucene in the experiments), T s is the set of all the terms in s , \(AL(C)\) is the average length of all the documents in C , \(f\left({t}_{j},s\right)\) is the term frequency of \({t}_{j}\) in s , \(idf\left({t}_{j}\right)\) is the inverse document frequency of \({t}_{j}\) in collection C with all the seed papers. \(idf\left({t}_{j}\right)\) is defined as
in which \(\left|C\right|\) is the number of papers in \(C\) , and \(\left|C({t}_{j})\right|\) is the number of papers in C satisfying the condition that \({t}_{j}\) appears in them. For a paper p and a research area \({a}_{k}\) , we can calculate the average similarity score between p and all the seed papers in C ( \({a}_{k}\) ) as
where C ( \({a}_{k}\) ) is the collection of seed papers in area \({a}_{k}\) .
We also consider citations between \(p\) and any of the papers in C . Citations in two different directions are considered separately: \(citingNum\left( p,{a}_{k}\right)\) denotes the number of papers in C ( \({a}_{k}\) ) that p cites, and \(citedNum\left(p,{a}_{k}\right)\) denotes the number of papers in C ( \({a}_{k}\) ) that cites p . Now we want to combine the three features. Normalization is required. For example, \(sim\left({p,a}_{k}\right)\) can be normalized by
in which \(RA\) is the set of 26 research areas. \(citingNum\left(p,{a}_{k}\right)\) and \(citedNum\left(p,{a}_{k}\right)\) can be normalized similarly. Then we let
for any \({a}_{k}\in RArea\) , in which \({\beta }_{1}\) , \({\beta }_{2}\) , and \({\beta }_{3}\) are three parameters. When applying Eq. 5 to \(p\) and all 26 research areas, we may obtain corresponding scores for each area. p can be put to research area \({a}_{k}\) if \(score\left(p,{a}_{k}\right)\) is the biggest among all 26 scores for all research areas. The values of \({\beta }_{1}\) , \({\beta }_{2}\) , and \({\beta }_{3}\) are decided by Euclidean Distance with multiple linear regression with a training data set (Wu et al., 2023 ). Compared with other similar methods such as Stacking with MLS and StackingC, this method can achieve comparable performance but much more efficient than the others. It should be very suitable for large-scale datasets.
In this study, we assume that each paper just belongs to one of the research areas. If required, this method can be modified to support multi-label classification, then a paper may belong to more than one research area at the same time. We may set a reasonable threshold \(\tau\) , and for any testing paper \(p\) and research area \({a}_{k}\) , if \(score\left(p,{a}_{k}\right)>\tau\) , then paper \(p\) belongs to research area \({a}_{k}\) . However, this is beyond the scope of this research, and we leave it for further study.
In summary, the proposed classification algorithm instance-based learning (IBL) is sketched as follows:
As we observed that papers in the same research area may have different citation patterns, it is better to treat them using multiple prediction models rather than one unified model. Therefore, for all the papers in a research area, we divide them into up to 10 groups according to the number of citations already obtained in the first m years. In a specific research area, for a group of papers considered, we count the number of citations they obtained during a certain period. We use cc ( i ) to represent the number of papers cited i times, where i ranges from 0 to n .
A threshold of 100 is set. We consider the values of cc (0), cc (1),…, cc ( n ) in order. If cc (0) is greater than or equal to the threshold, we create a group with those papers that received zero citations. Otherwise, we combine cc (0) with cc (1), and if the sum is still less than the threshold, we continue adding the next value cc (2), and so on, until the cumulative sum reaches or exceeds the threshold. At this point, we create a group with all the papers contributing to that cumulative sum. We then move on to the next unassigned value of cc ( i ) and repeat the process, creating new groups until all papers are assigned to a group. The last group may contain fewer than 100 papers, but it is still considered a valid group.
A regression model is set for each of these groups for prediction. For the training data set, all the papers are classified by research area with known citation history of up to t years. For all the papers belonging to a group \({g}_{i}\) inside a research area \({a}_{k}\) , we put their information together. Consider
\({c}_{0}\) , \({c}_{1}\) , …, \({c}_{m}\) , and \({\text{c}}_{t}\) are citation counts of all the papers involved up to year 0, 1,…, m , and in year t ( t ≥ m ). We can train the weights \({\text{w}}_{0}\) , \({\text{w}}_{1}\) ,…, \({\text{w}}_{m}\) , and b for this group by multiple linear regression using \({c}_{0}\) , \({c}_{1}\) , …, \({c}_{m}\) as independent variables and \(c^{\prime}_{t}\) as the target variable. The same applies to all other groups and research areas.
To predict the future citation counts of a paper, we need to decide which research area and group that paper should be in. Then the corresponding model can be chosen for the prediction. Algorithm MM is sketched as follows:
Note that classification and citation count prediction are two separate tasks. When performing the citation count prediction task, it is required that all the papers involved should have a research area label. Such a requirement can be satisfied in different ways. For example, in the WoS system, it has a list of journals, and each journal is assigned to one or two research areas. All the papers published in those journals are classified by the journals publishing them. In arXiv, an open-access repository of scientific papers, all the papers are assigned a research area label by the authors when uploading them. When performing the citation count prediction task on such datasets, we do not need to do anything else. However, for papers in DBLP, all the papers are not classified. It is necessary to classify them in some way before we can perform citation count prediction for all the papers involved. In this study, we proposed an instance-based learning approach, which provides an efficient and effective solution to this problem.
Two datasets were used for this study. One is a DBLP dataset, and other is an arXiv dataset.
We downloaded a DBLP dataset (Tang et al., 2008 ). Footnote 4 It contains 4,107,340 papers in computer science and 36,624,464 citations from 1961 to 2019. For every paper, the dataset provides its metadata, such as title, abstract, references, authors and their affiliations, publication year, the venue in which the paper was published, and citations since publication. Some subsets of it were used in this study.
For the classification part, we used two subsets of the dataset. The first one ( C 1 ) is all the papers published in those 72 recommended venues in CSRankings between 1965 and 2019. There are 191,727 papers. C 1 is used as seed papers for all 26 research areas. The second subset ( C 2 ) includes 1300 papers, 50 for each research area. Those papers were randomly selected from a group of 54 conferences and journals and judged manually. C 2 is used for the testing of the proposed classification method.
For the prediction part, we also used two subsets. One subset for training and the other for testing. The training dataset ( C 3 ) includes selected papers published between 1990 and 1994, and the testing dataset ( C 4 ) includes selected papers published in 1995. For all those papers between 1990 and 1994 or in 1995, we removed those that did not get any citation and those with incomplete information. After such processing, we obtain 38,247 papers for dataset C 3 , and 9967 papers for dataset C 4 .
We also downloaded an arXiv dataset (Saier and Farber, 2020 ). Footnote 5 It contains 1,043,126 papers in many research areas including Physics, Mathematics, Computer Science, and others, with 15,954,664 citations from 1991 to 2022. For every paper, its metadata such as title, abstract, references, authors and affiliations, publication year, and citations since publication was provided. Importantly, each paper is given a research area label by the authors. Therefore, it is no need to classify papers when we use this dataset for citation count prediction. Two subsets were generated in this study. One subset for training and the other for testing. The training dataset ( C 5 ) includes all the papers published between 2008 and 2013, and the testing dataset ( C 6 ) includes all the papers published in 2014. There are 5876 papers in dataset C 5 and 1471papers in dataset C 6 .
In the CSRankings classification system, there are a total of 26 special research areas. A few top venues are recommended for each of them. We assume that all the papers published in those recommended conferences belong to the corresponding research area solely. For example, three conferences CVPR, ECCV, and ICCV are recommended for Computer Vision. We assume that all the papers published in these three conferences belong to the Computer Vision research area but no others.
To evaluate the proposed method, we used a set of 1300 non-seed papers ( C 2 ). It included 50 papers for each research area. All of them were labelled manually. In Eq. 5 , three parameters need to be trained. Therefore, we divided those 1300 papers into two equal partitions of 650, and each included the same number of papers in every research area. Then the two-fold cross-validation was performed. Table 1 shows the average performance.
We can see that the proposed method with all three features, content similarity (Sim), citation to other papers (To evaluate the proposed method, we used a set of 1300 non-seed papers ( C 2 ). It included 50 papers for each research area. All of them were labelled manually. In Eq. 5 , three parameters need to be trained. Therefore, we divided those 1300 papers into two equal partitions of 650, and each included the same number of papers in every research area. Then the two-fold cross-validation was performed. Table 1 shows the average performance.
We can see that the proposed method with all three features, content similarity ( \(sim\) ), citation to other papers ( \(citingNum\) ), and citation by others ( \(citedNum\) ), are useful for the classification task. Roughly citation in both directions ( \(citingNum+citedNum\) ) and content similarity ( \(sim\) ) have the same ability. Considering three features together, we can obtain an accuracy, or an F-measure, of approaching 0.8. We are satisfied with this solution. On the one hand, its classification performance is good compared with other methods in the same category, e.g., (Ambalavanan & Devarakonda, 2020 ; Kandimalla et al., 2020 ). In Kandimalla et al. ( 2020 ), F-scores across 81 subject categories are between 0.5 and 0.8 (See Fig. 1 in that paper). In Ambalavanan and Devarakonda ( 2020 ), the four models ITL, Cascade Learner, Ensemble-Boolean, and Ensemble-FFN obtain an F-score of 0.553, 0.753, 0.628, and 0.477, respectively, on the Marshall dataset they experimented with (see Table 4 in their paper). Although those results may not be comparable since the datasets used are different, it is an indicator that our method is very good. Besides, our method can be implemented very efficiently. When the seed papers are indexed, we can deal with a large collection of papers very quickly with very little resource. The method is very scalable.
For the proposed method MM, we set 10 as the number of groups in each research area for the DBLP dataset, and 5 for the arXiv dataset. This is mainly because the arXiv dataset is smaller and has fewer papers in each research area.
Apart from MM, five baseline prediction methods were used for comparison:
Mean of early years (MEY). It is a simple prediction function which returns the average of early citations of the paper as its predicted citations in the future (Abrishami & Aliakbary, 2019 ).
AVR. Assume that there is a collection of papers with known citation history as the training data set. For a given paper for prediction, this method finds a group of most similar papers in the training set relating to their early citations (with the minimal sum of the squared citation count difference over the years), and then utilizes the average citations of those similar papers in the subsequent years as the predicted citation counts of the paper (Cao et al., 2016 ).
RNN adopts a Recurrent Neural Network to predict papers’ future citation counts based on their early citation data (Abrishami & Aliakbary, 2019 ).
OLS. Linear regression is used for the prediction model (Abramo et al., 2019 ). There are four variants. Both OLS_res and OLS_log only use early citations as independent variables in their prediction models, while OLS2_res and OLS2_log use early citations and impact factors of journals in their prediction models. OLS_res and OLS2_res apply a linear regularization to their early citations, while OLS_log and OLS2_log apply a logarithmic regularization to their early citations.
NCFCM adopts a neural network to predict papers’ future citation counts based on early citation data and two simple prediction model data (Wang et al., 2021 ).
Two popular metrics are used to evaluate the proposed method and compare it with the baselines: mean square error (MSE) and the coefficient of determination (R 2 ). For a given set of actual values Y \(=\{{y}_{1},{y}_{2},\dots ,{y}_{n}\}\) and set of predicted values \(\widehat{Y\boldsymbol{ }}=\{{\widehat{y}}_{1},{\widehat{y}}_{2},\dots ,{\widehat{y}}_{n}\}\) , MSE and R 2 are defined as follows:
where \({\overline{y} }_{i}\) is the average of all n values in y . MSE measures the variation of the predicted values from the actual values, thus smaller values of MSE are desirable. R 2 measures the corelation between the predicted values and actual values, and its value is between 0 and 1, where R 2 = 0 means no correlation, R 2 = 1 means a perfect positive correlation between the predicted values and the actual values, thus larger values of R 2 are desirable.
Evaluation has been carried out on two different aspects: overall performance for all the papers and for 100 highly citated papers.
For papers with 0–5 years of citation history, we predict their citation counts in three continuous years in the future. The results are shown in Tables 2 , 3 , 4 , 5 , 6 , 7 . “Zero years of early citation data” means that the prediction was made in the same year as the paper was published. “One year of early citation data” means that the prediction was made in the next year as the paper was published. The number in bold indicates the best performance.
One can see that MM performs the best in most cases. In a few cases, OLS2_res performs the best. This is because OLS2_res considers both the paper’s early citation history and the journal’s impact factor, and the latter is not considered in any other method. In this way, it gives OLS2_res some advantages, especially when the citation history is very short. In a few cases, RNN performs the best on the arXiv dataset. In one case, AVE and NCFCM tied for first place in R 2 . Because linear regression is used in both OLS_res and MM, a comparison between them is able to show that dividing papers into multiple research areas is a very useful strategy for us to obtain better prediction performance. See “ Ablation Study of MM ” section for further experiments and analysis.
An important application of citation prediction is the early detection of highly cited papers (Abrishami & Aliakbary, 2019 ). Therefore, we evaluate the performance of the proposed method and its competitors in predicting highly cited papers. Based on the total citation counts in 2000 (DBLP) and in 2019 (arXiv), 100 most cited papers were selected for prediction. For all the papers involved, we compute the MSE values between the predicted citation counts and the actual citation counts of them. The results are shown in Table 8 .
From Table 8 , one can see that MM performs better than all the others, except when k = 0 (which means zero years of early citation data) OLS2_res performs slightly better than MM in the DBLP dataset. In all other cases, MM outperforms the competing methods.
MM mainly incorporates two factors including research area and early citation counts into consideration. It is desirable to find how these two factors impact prediction performance. Another angle is the number of groups divided in each research area. To find out the impact of these features on prediction performance, we define some variants that implement none or one of the features of MM.
MM-RA (RA). A variant of the MM algorithm that only considers research area but not early citation counts.
MM-CC (CC). A variant of the MM algorithm that only considers early citation counts.
MM-5. A variant of the MM algorithm that divides all the papers in the area into 5 instead of 10 groups.
MEY. It is a simplest variant of MM. It considers neither research area nor early citation counts.
Now let us have a look at how these variants perform compared with the original algorithm. See Tables 9 , 10 , 11 , 12 , 13 , 14 , 15 for the results. It is not surprising that MM performs better than three variants of MM including RA, CC and MEY, while MEY, the variant with none of the two components, performs the worst in predicting the citation counts of papers. Such a phenomenon demonstrates that both components including research area and early citation counts are useful for prediction performance, either used separately or in combination. However, the usefulness of these two components is not the same. The performance of CC is not as good as RA when k = 0 and k = 1, but better than RA when k > 1. Understandably, this indicates that RA is a more useful resource than citation history when the citation history is short, but the latter becomes more and more useful when the citation history becomes longer.
When applying the standard MM algorithm, we divide all the papers in one research area into 10 groups based on the number of citations they obtain in the early years. MM-5 reduces the number of groups from 10 to 5 simply by combining two neighbouring groups to one. MM is better than MM-5 in most cases and on average. The difference between them is small in most cases. However, it is noticeable that MM-5 performs better than MM in two cases, mainly because the size of some of the groups is very small, and the prediction based on such small groups is not very accurate.
For the DBLP dataset, some papers were classified automatically through the venues in which they were published, while many others were classified through the classification method IBL. It would be interesting to make a comparison of these two groups when performing the prediction task. The results are shown in Table 16 We can see that the group of non-seed papers gets better perdition results than the group of seed papers by a clear margin in all the cases. This demonstrates that the two methods IBL and MM can work together well for achieving good prediction results. On the other hand, such a result is a little surprising. Why can the non-seed group perform better than the seed group? One major reason is for the citation count prediction task, MSE values and citation counts have a strong positive correlation. In this case, there are 2346 seed papers ( C 7 ), whose average citation count is 6.339, while there are 7621 non-seed papers ( C 8 ), whose average citation count is 3.085. These two groups are not directly comparable because of the difference in average citation counts. Note that C 4 = C 7 + C 8 (see “ Datasets ” section for C 4 ’s definition).
To make the comparison fair to both parties, we select a subgroup from each of them by adding a restriction: those papers obtain a citation count in the range of [10,20] by the year 2000. We obtain 318 papers ( C 9 ) from the seed paper group and 418 papers ( C 10 ) from the non-seed paper group. Coincidentally, the average citation counts for both sub-groups are the same: 13.443. This time, the two groups are in a perfect condition for a comparison. Table 17 shows the results. Not surprisingly, those MSE value pairs are very close. It demonstrates that for papers either classified by our classification algorithm IBL, or categorized by recommended top venues, the prediction is equally good. It also implies that IBL can perform the classification work properly.
Different from previous studies, this paper applies multiple models to predict the citation counts of papers in the next couple of years, and each model fits a special research area and early citation history of the paper in question. The rationale behind this is: in general, papers in different research areas and with different early citation counts have their own citation patterns. To verify the prediction performance of the proposed method, we have tested it with two datasets taken from DBLP and arXiv. The experimental results show that the proposed MM method outperforms all the baseline methods involved in most cases in two tasks: the overall prediction performance of a large collection of paper and prediction performance of a group of highly cited papers.
As an important component of prediction for research papers, we have also presented a novel instance-based learning model for classification of research papers. By predefining a small group of papers in each category, the proposed method can classify new papers very efficiently with good accuracy.
As our future work, we would incorporating other types of information such as publication venues and author information. Then, prediction performance can be further improved. Secondly, we plan to explore using some deep learning methods for research paper classification. For example, such methods can be used to compare the content similarity of two research papers.
https://ref.ac.uk/2014/
https://CSRankings.org
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http://doi.org/10.5281/zenodo.3385851
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Fang Zhang & Shengli Wu
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All authors contributed to the study conception and design. Data collection, programming and analysis were performed by Fang Zhang. The first draft of the manuscript was written by Shengli Wu, and all authors read and approved the final manuscript.
Correspondence to Shengli Wu .
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Zhang, F., Wu, S. Predicting citation impact of academic papers across research areas using multiple models and early citations. Scientometrics (2024). https://doi.org/10.1007/s11192-024-05086-0
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Nurse practitioners are clinical leaders in their specialty field contributing to the development and changes in policies and procedures, initiating research based on identified need, and providing clinical expertise in managing complex care, among other numerous skills and standards.1 Considering the significant increase in the need for Child and Youth Mental Health Services (CYMHS), particularly since the pandemic,2 it would be remiss not to utilise such a resource in ameliorating the current burden on the system. The nurse practitioner in CYMHS is an anomaly, necessitating further research and exploration to illustrate their value and scope to be harnessed and supported.3
Keywords: Child and Youth Mental Health, nurse practitioner, Mental Health, Australia
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Working with sources is an important skill that you’ll need throughout your academic career.
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Once you have found information that you want to include in your paper, signal phrases can help you to introduce it. Here are a few examples:
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You present the author’s position neutrally, without any special emphasis. | recent research, food services are responsible for one-third of anthropogenic greenhouse gas emissions. | According to, analyzes, asks, describes, discusses, explains, in the words of, notes, observes, points out, reports, writes |
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When using APA format, follow the author-date method of in-text citation. This means that the author's last name and the year of publication for the source should appear in the text, like, for example, (Jones, 1998). One complete reference for each source should appear in the reference list at the end of the paper.
At college level, you must properly cite your sources in all essays, research papers, and other academic texts (except exams and in-class exercises). Add a citation whenever you quote, paraphrase, or summarize information or ideas from a source. You should also give full source details in a bibliography or reference list at the end of your text.
The point of an in-text citation is to show your reader where your information comes from. Including citations: Avoids plagiarism by acknowledging the original author's contribution. Allows readers to verify your claims and do follow-up research. Shows you are engaging with the literature of your field.
Articles & Research Databases Literature on your research topic and direct access to articles online, when available at UW.; E-Journals Alphabetical list of electronic journal titles held at UW.; Encyclopedias & Dictionaries Resources for looking up quick facts and background information.; E-Newspapers, Media, Maps & More Recommendations for finding news, audio/video, images, government ...
Basic Principles of Citation. APA Style uses the author-date citation system, in which a brief in-text citation directs readers to a full reference list entry. The in-text citation appears within the body of the paper (or in a table, figure, footnote, or appendix) and briefly identifies the cited work by its author and date of publication.
Scholarship is a conversation and scholars use citations not only to give credit to original creators and thinkers, but also to add strength and authority to their own work.By citing their sources, scholars are placing their work in a specific context to show where they "fit" within the larger conversation.Citations are also a great way to leave a trail intended to help others who may want ...
Figure 8.1 in Chapter 8 of the Publication Manual provides an example of an appropriate level of citation. The number of sources you cite in your paper depends on the purpose of your work. For most papers, cite one or two of the most representative sources for each key point. Literature review papers typically include a more exhaustive list of ...
The Bluebook: A Uniform System of Citation is the main style guide for legal citations in the US. It's widely used in law, and also when legal materials need to be cited in other disciplines. Bluebook footnote citation. 1 David E. Pozen, Freedom of Information Beyond the Freedom of Information Act, 165, U. P🇦 .
Have fewer than 40 words, use quotation marks around the quote, are incorporated into the text of the paper. (Shayden, 2016, p. 202) Long quotes: Have 40 words or MORE, DO NOT use quotation marks, are in a block quote (by indenting 0.5" or 1 tab) beneath the text of the paragraph. (Miller et al., 2016, p. 136) Quotes for webpages:
Author-date citations are used in APA and Chicago styles and list the author's last name, publication date, and page number(s). Author-page citations are used in MLA and include the author's last name and page number(s). Each citation style has different formatting rules, so be sure to double-check the style guide for the style you are using.
A list of sources can be a useful record for further research; Different academic disciplines prefer different citation styles, most commonly APA and MLA styles. Besides these styles, there are Chicago, Turabian, AAA, AP, and more. Only use the most current edition of the citation style.
A citation identifies for the reader the original source for an idea, information, or image that is referred to in a work. In the body of a paper, the in-text citation acknowledges the source of information used.; At the end of a paper, the citations are compiled on a References or Works Cited list.A basic citation includes the author, title, and publication information of the source.
In this situation the original author and date should be stated first followed by 'as cited in' followed by the author and date of the secondary source. For example: Lorde (1980) as cited in Mitchell (2017) Or (Lorde, 1980, as cited in Mitchell, 2017) Back to top. 3. How to Cite Different Source Types.
The Chicago/Turabian style of citing sources is generally used when citing sources for humanities papers, and is best known for its requirement that writers place bibliographic citations at the bottom of a page (in Chicago-format footnotes) or at the end of a paper (endnotes). The Turabian and Chicago citation styles are almost identical, but ...
Articles & Research Databases Literature on your research topic and direct access to articles online, when available at UW.; E-Journals Alphabetical list of electronic journal titles held at UW.; Encyclopedias & Dictionaries Resources for looking up quick facts and background information.; E-Newspapers, Media, Maps & More Recommendations for finding news, audio/video, images, government ...
There are two main kinds of titles. Firstly, titles can be the name of the standalone work like books and research papers. In this case, the title of the work should appear in the title element of the reference. Secondly, they can be a part of a bigger work, such as edited chapters, podcast episodes, and even songs.
When you cite a source with up to three authors, cite all authors' names. For four or more authors, list only the first name, followed by ' et al. ': Number of authors. In-text citation example. 1 author. (Davis, 2019) 2 authors. (Davis and Barrett, 2019) 3 authors.
Research paper: In-text citation: Use superscript numbers to cite sources in the text, e.g., "Previous research has shown that^1,2,3…". Reference list citation: Format: Author (s). Title of paper. In: Editor (s). Title of the conference proceedings. Place of publication: Publisher; Year of publication. Page range.
3. List the title of the research paper. Use sentence capitalization to write out the full title of the research paper, capitalizing the first word and any proper names. If it has a subtitle, place a colon and capitalize the first word of the subtitle. [3] For example: "Kringle, K., & Frost, J. (2012).
Parenthetical citation: According to new research … (Smith, 2020). Narrative citation: Smith (2020) notes that … Multiple authors and corporate authors. The in-text citation changes slightly when a source has multiple authors or an organization as an author. Pay attention to punctuation and the use of the ampersand (&) symbol.
Citation in research papers: A citation appears in the main text of the paper. It is a way of giving credit to the information that you have specifically mentioned in your research paper by leading the reader to the original source of information. You will need to use citation in research papers whenever you are using information to elaborate a ...
Chicago or Turabian. Students and researchers commonly use the Chicago Manual of Style guide, or Turabian, for most real-world subjects in magazines, books, newspapers and many other non-scholarly publications. Example of Chicago style for a book with one author: Doe, John. 1999. Causes of the Civil War.
The rules for citing the use of generative AI, both textual and visual, are still evolving. For guidelines on when to cite the use of AI, please refer to the section on Academic Integrity. ... For academic papers or research articles, you should have a numbered footnote or endnote. Footnote - prompt not included in the text of the paper. 1 ...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations-citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the ...
I know citations are different and should be more visible quicker in the land of research, but the same logic applies. Patience—and continued hard work (i.e., to advance one's research acumen ...
As the volume of scientific literature expands rapidly, accurately gauging and predicting the citation impact of academic papers has become increasingly imperative. Citation counts serve as a widely adopted metric for this purpose. While numerous researchers have explored techniques for projecting papers' citation counts, a prevalent constraint lies in the utilization of a singular model ...
Abstract. Nurse practitioners are clinical leaders in their specialty field contributing to the development and changes in policies and procedures, initiating research based on identified need, and providing clinical expertise in managing complex care, among other numerous skills and standards.1 Considering the significant increase in the need for Child and Youth Mental Health Services (CYMHS ...
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This paper focuses on the thermophysical property mixture model approach that makes use of NASA's polynomial fits of species and another cryogenic model chosen from NIST's Refprop program. Presented in this paper are results of a 1D denotation CFD simulation of stoichiometric Hydrogen and Oxygen mixture at a cryogenic upstream condition.
Recent research suggests that greater in-flight running intensity and volume attenuate decrements in aerobic capacity and strength; however, this has not been experimentally confirmed. The Exploration Exercise Treadmill Requirements study is currently underway, aiming to determine the effects of exercising without a treadmill on aerobic ...