make hypothesis collocation

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A completely new type of dictionary with word collocation that helps students and advanced learners effectively study, write and speak natural-sounding English . This online dictionary is very helpful for the education of the IELTS, TOEFL test.

• Collocations/collocation - common word combinations such as 'bright idea' or 'talk freely' - are the essential building blocks of natural-sounding English. The dictionary contains over 150,000 collocations for nearly 9,000 headwords.
• The dictionary shows words commonly used in combination with each headword: nouns, verbs, adjectives, adverbs, and prepositions, common phrases.
• The collocation dictionary is based on 100 million word British National Corpus.
• Over 50,000 examples show how the collocation/collocations are used in context, with grammar and register information where helpful.
• The clear page layout groups collocations according to part of speech and meaning, and helps users pinpoint speedily the headword, sense and collocation they need.
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Collocation của từ.

acceptable, plausible | bold

| unlikely | speculative | testable | working

| scientific

VERB + HYPOTHESIS

construct, form, formulate, have, make, propose, put forward, suggest

| consider, discuss, examine, test (out)

| confirm, prove, support | accept | reject

concern sth

| predict sth

Collocation & Expressions with MAKE and DO (PDF)

This lesson deals with collocations with make and do , two verbs that many learners have problems with. The English verbs do and make are highly confused, so pay particular attention to the examples down below. You can also download collocations with make and do PDF at the end of this post.

If you remember that the basic meaning of make is about producing something and the basic meaning of do is about performing an action , then the collocations on this page may seem more logical.

Collocation with MAKE and DO

Check Also: 100+ Common English Collocations List (PDF) Types of Collocations in English & Examples Separable and Inseparable Phrasal Verbs & List (PDF)✅

Collocation Examples with MAKE

Collocation Examples with DO

Tip: Notice all the patterns that you can see in these tables. For example, make a comment, make an excuse and make a contribution to a discussion are all connected with saying things . Noticing connections like this may help you to remember the correct collocation.

Collocations with Make and Do PDF

  Collocations with MAKE and DO PDF – download

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The English language is considered one of the hardest languages to learn. It is rife with similarly spelled words with completely different meanings and complex sentences. There are hard and fast rules to the language, then there are bits of language that have no rhyme or reason, making even native English language speakers scratch their heads and wonder if they’re right or not.

Collocations fall in the latter category. If you’ve never heard of this term, you’re not alone. It’s not something taught in detail in the average English curriculum. A collocation is defined as two or more words that join together to form a unique meaning that is readily understood by English speakers but probably not by those who speak it as a second language. In many cases, the speaker isn’t aware of the collocation. They’re simply repeating a phrase they’ve heard during prolonged exposure to the English language.

While the scientific community is composed of different nations and languages, journals are primarily published in English. English was adopted centuries ago as the communal and universal language for scholars and researchers, back when it was the most common second language learned by those in medical and business industries.

Of course, a vast percentage of researchers aren’t native English speakers, which means that many higher education articles are written with English as the author’s second language. This can cause translation problems, particularly when using collocations, which can’t be replaced with a synonym. 

Understanding Collocations

There are two types of collocations - a weak one and a strong one. A collocation is pairing one or more words together to create a particular meaning.  A weak collocation includes a word that pairs with many other words within the English language. 

A strong collocation is comprised of at least one word that doesn’t pair well with others. For example, blonde hair is a strong collocation because blonde doesn’t pair with many other English words.

How to Write a Collocation

Collocations can be difficult to teach and to learn. They are inherently part of a native English speaker’s language to the point they may not even notice the collocation. There may not be strict rules regarding collocations, but there are guidelines to follow instead of mashing two random words together.

Of course, to non-native English speakers, the words in the collocation will seem like randomized word pairings. There are at least six types of collocations.

Adjective + noun

Example: She was in excruciating pain after the car accident. 

Noun + verb

Example: People in the South are relieved when temperatures fall .

Verb + noun  

Example: The happy couple couldn’t wait to get married and spend their lives together.

Verb + adverb

Example: I can vaguely remember her face, but not her name.

Adverb + adjective

Example: She was completely satisfied with the house renovations.

Noun + noun

Example: He felt a surge of anger when a classmate plagiarized his work.

Academic writing is much more complex than the above examples. How should scholars incorporate collocations into their academic papers?

Common Collocation Phrases Used in Academics and Their Improper Counterparts

A scholar or researcher likely defaults to their academic vocabulary when comprising their findings into a research paper. The text of the paper must be accessible to all readers, even when explaining complex ideas. Collocations are commonly used in academic papers.

But those common phrases won’t have the same meaning if replaced with a synonym. What are some of the most frequent collocations used in academic writing? And which phrases don’t pair well together? Here’s one example of an academic collocation:

Correct Collocation: A scholar’s job is to search for evidence that supports their hypothesis.

Improper Collocation: A scholar’s job is to research evidence that supports their hypothesis.

If you’re unsure about how to use collocations, several online collocation dictionaries can help.

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Definition of hypothesis noun from the Oxford Advanced American Dictionary

• formulate/advance a theory/hypothesis
• build/construct/create/develop a simple/theoretical/mathematical model
• develop/establish/provide/use a theoretical/conceptual framework/an algorithm
• advance/argue/develop the thesis that…
• explore an idea/a concept/a hypothesis
• make a prediction/an inference
• base a prediction/your calculations on something
• investigate/evaluate/accept/challenge/reject a theory/hypothesis/model
• design an experiment/a questionnaire/a study/a test
• do research/an experiment/an analysis
• make observations/calculations
• take/record measurements
• carry out/conduct/perform an experiment/a test/a longitudinal study/observations/clinical trials
• run an experiment/a simulation/clinical trials
• repeat an experiment/a test/an analysis
• replicate a study/the results/the findings
• observe/study/examine/investigate/assess a pattern/a process/a behavior
• fund/support the research/project/study
• seek/provide/get/secure funding for research
• collect/gather/extract data/information
• yield data/evidence/similar findings/the same results
• analyze/examine the data/soil samples/a specimen
• consider/compare/interpret the results/findings
• fit the data/model
• confirm/support/verify a prediction/a hypothesis/the results/the findings
• prove a conjecture/hypothesis/theorem
• draw/make/reach the same conclusions
• read/review the records/literature
• describe/report an experiment/a study
• present/publish/summarize the results/findings
• present/publish/read/review/cite a paper in a scientific journal

Want to learn more?

Find out which words work together and produce more natural-sounding English with the Oxford Collocations Dictionary app. Try it for free as part of the Oxford Advanced Learner’s Dictionary app.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Prevent plagiarism. Run a free check.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved June 7, 2024, from https://www.scribbr.com/methodology/hypothesis/

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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

• proposition
• supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

• counter - hypothesis
• nebular hypothesis
• null hypothesis
• planetesimal hypothesis
• Whorfian hypothesis

Articles Related to hypothesis

This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

Dictionary Entries Near hypothesis

hypothermia

hypothesize

Cite this Entry

“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 8 Jun. 2024.

Kids Definition

Kids definition of hypothesis, medical definition, medical definition of hypothesis, more from merriam-webster on hypothesis.

Nglish: Translation of hypothesis for Spanish Speakers

Britannica English: Translation of hypothesis for Arabic Speakers

Britannica.com: Encyclopedia article about hypothesis

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7.1: Collocates

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• Page ID 81928

• Anatol Stefanowitsch
• Freie Universität Berlin via Language Science Press

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The (orthographic) word plays a central role in corpus linguistics. As suggested in Chapter 4, this is in no small part due to the fact that all corpora, whatever additional annotations may have been added, consist of orthographically represented language. This makes it easy to retrieve word forms. Every concordancing program offers the possibility to search for a string of characters – in fact, some are limited to this kind of query.

However, the focus on words is also due to the fact that the results of corpus linguistic research quickly showed that words (individually and in groups) are more interesting and show a more complex behavior than traditional, grammar-focused theories of language assumed. An area in which this is very obvious, and which has therefore become one of the most heavily researched areas in corpus linguistics, is the way in which words combine to form so-called collocations .

This chapter is dedicated entirely to the discussion of collocation. At first, this will seem like a somewhat abrupt shift from the topics and phenomena we have discussed so far – it may not even be immediately obvious how they fit into the definition of corpus linguistics as “the investigation of linguistic research questions that have been framed in terms of the conditional distribution of linguistic phenomena in a linguistic corpus”, which was presented at the end of Chapter 2. However, a closer look will show that studying the co-occurrence of words and/ or word forms is simply a special case of precisely this kind of research program.

7.1 Collocates

Trivially, texts are not random sequences of words. There are several factors influencing the likelihood of two (or more) words occurring next to each other.

First, the co-occurrence of words in a sequence is restricted by grammatical considerations. For example, a definite article cannot be followed by another definite article or a verb, but only by a noun, by an adjective modifying a noun, by an adverb modifying such an adjective or by a post-determiner. Likewise, a transitive verb requires a direct object in the form of a noun phrase, so – barring cases where the direct object is pre- or post-posed – it will be followed by a word that can occur at the beginning of a noun phrase (such as a pronoun, a determiner, an adjective or a noun).

Second, the co-occurrence of words is restricted by semantic considerations. For example, the transitive verb drink requires a direct object referring to a liquid, so it is probable that it will be followed by words like water , beer , coffee , poison , etc., and improbable that it will be followed by words like bread , guitar , stone , democracy , etc. Such restrictions are treated as a grammatical property of words (called selection restrictions ) in some theories, but they may also be an expression of our world knowledge concerning the activity of drinking.

Finally, and related to the issue of world knowledge, the co-occurrence of words is restricted by topical considerations. Words will occur in sequences that correspond to the contents we are attempting to express, so it is probable that co-occurring content words will come from the same discourse domain.

However, it has long been noted that words are not distributed randomly even within the confines of grammar, lexical semantics, world knowledge, and communicative intent. Instead, a given word will have affinities to some words, and disaffinities to others, which we could not predict given a set of grammatical rules, a dictionary and a thought that needs to be expressed. One of the first principled discussions of this phenomenon is found in Firth (1957). Using the example of the word ass (in the sense of ‘donkey’), he discusses the way in which what he calls habitual collocations contribute to the meaning of words:

One of the meanings of ass is its habitual collocation with an immediately preceding you silly , and with other phrases of address or of personal reference. ... There are only limited possibilities of collocation with preceding adjectives, among which the commonest are silly , obstinate , stupid , awful , occasionally egregious . Young is much more frequently found than old . (Firth 1957: 194f)

Note that Firth, although writing well before the advent of corpus linguistics, refers explicitly to frequency as a characteristic of collocations. The possibility of using frequency as part of the definition of collocates, and thus as a way of identifying them, was quickly taken up. Halliday (1961) provides what is probably the first strictly quantitative definition (cf. also Church & Hanks (1990) for a more recent comprehensive quantitative discussion):

Collocation is the syntagmatic association of lexical items, quantifiable, textually, as the probability that there will occur, at n removes (a distance of n lexical items) from an item x, the items a, b, c... Any given item thus enters into a range of collocation, the items with which it is collocated being ranged from more to less probable... (Halliday 1961: 276)

7.1.1 Collocation as a quantitative phenomenon

Essentially, then, collocation is just a special case of the quantitative corpus linguistic research design adopted in this book: to ask whether two words form a collocation (or: are collocates of each other) is to ask whether one of these words occurs in a given position more frequently than expected by chance under the condition that the other word occurs in a structurally or sequentially related position. In other words, we can decide whether two words a and b can be regarded as collocates on the basis of a contingency table like that in Table 7.1. The FIRST POSITION in the sequence is treated as the dependent variable, with two values: the word we are interested in (here: WORD A), and all OTHER words. The SECOND POSITION is treated as the independent variable, again, with two values: the word we are interested in (here: WORD B), and all OTHER words (of course, it does not matter which word we treat as the dependent and which as the independent variable, unless our research design suggests a particular reason). 1

Table 7.1: Collocation

On the basis of such a table, we can determine the collocation status of a given word pair. For example, we can ask whether Firth was right with respect to the claim that silly ass is a collocation. The necessary data are shown in Table 7.2: As discussed above, the dependent variable is the FIRST POSITION in the sequence, with the values SILLY and ¬SILLY (i.e., all words that are not ass ); the independent variable is the SECOND POSITION in the sequence, with the values ASS and ¬ASS.

Table 7.2: Co-occurrence of silly and ass in the BNC

The combination silly ass is very rare in English, occurring just seven times in the 98 363 783 word BNC, but the expected frequencies in Table 7.2 show that this is vastly more frequent than should be the case if the words co-occurred randomly – in the latter case, the combination should have occurred just 0.01 times (i.e., not at all). The difference between the observed and the expected frequencies is highly significant (χ 2 = 6033.8, df = 1, p < 0.001). Note that we are using the χ 2 test here because we are already familiar with it. However, this is not the most useful test for the purpose of identifying collocations, so we will discuss better options below.

Generally speaking, the goal of a quantitative collocation analysis is to identify, for a given word, those other words that are characteristic for its context of usage. Tables 7.1 and 7.2 present the most straightforward way of doing so: we simply compare the frequency with which two words co-occur to the frequencies with which they occur in the corpus in general. In other words, the two conditions across which we are investigating the distribution of a word are “next to a given other word” and “everywhere else”. This means that the corpus itself functions as a kind of neutral control condition, albeit a somewhat indiscriminate one: comparing the frequency of a word next to some other word to its frequency in the entire rest of the corpus is a bit like comparing an experimental group of subjects that have been given a particular treatment to a control group consisting of all other people who happen to live in the same city.

Often, we will be interested in the distribution of a word across two specific conditions – in the case of collocation, the distribution across the immediate contexts of two semantically related words. It may be more insightful to compare adjectives occurring next to ass with those occurring next to the rough synonym donkey or the superordinate term animal . Obviously, the fact that silly occurs more frequently with ass than with donkey or animal is more interesting than the fact that silly occurs more frequently with ass than with stone or democracy . Likewise, the fact that silly occurs with ass more frequently than childish is more interesting than the fact that silly occurs with ass more frequently than precious or parliamentary .

In such cases, we can modify Table 7.1 as shown in Table 7.3 to identify the collocates that differ significantly between two words. There is no established term for such collocates, so we we will call them differential collocates here 2 (the method is based on Church et al. 1991).

Table 7.3: Identifying differential collocates

Since the collocation silly ass and the word ass in general are so infrequent in the BNC, let us use a different noun to demonstrate the usefulness of this method, the word game. We can speak of silly game(s) or childish game(s) , but we may feel that the latter is more typical than the former. The relevant lemma frequencies to put this feeling to the test are shown in Table 7.4.

Table 7.4: Childish game vs. silly game (lemmas) in the BNC

The sequences childish game(s) and silly game(s) both occur in the BNC. Both combinations taken individually are significantly more frequent than expected (you may check this yourself using the frequencies from Table 7.4, the total lemma frequency of game in the BNC (20 627), and the total number of words in the BNC given in Table 7.2 above). The lemma sequence silly game is more frequent, which might lead us to assume that it is the stronger collocation. However, the direct comparison shows that this is due to the fact that silly is more frequent in general than childish , making the combination silly game more probable than the combination childish game even if the three words were distributed randomly. The difference between the observed and the expected frequencies suggests that childish is more strongly associated with game(s) than silly . The difference is significant (χ 2 = 6.49, df = 1, p < 0.05).

Researchers differ with respect to what types of co-occurrence they focus on when identifying collocations. Some treat co-occurrence as a purely sequential phenomenon defining collocates as words that co-occur more frequently than expected within a given span. Some researchers require a span of 1 (i.e., the words must occur directly next to each other), but many allow larger spans (five words being a relatively typical span size).

Other researchers treat co-occurrence as a structural phenomenon, i.e., they define collocates as words that co-occur more frequently than expected in two related positions in a particular grammatical structure, for example, the adjective and noun positions in noun phrases of the form [Det Adj N] or the verb and noun position in transitive verb phrases of the form [V [ NP (Det) (Adj) N]]. 3 However, instead of limiting the definition to one of these possibilities, it seems more plausible to define the term appropriately in the context of a specific research question. In the examples above, we used a purely sequential definition that simply required words to occur next to each other, paying no attention to their word-class or structural relationship; given that we were looking at adjective-noun combinations, it would certainly have been reasonable to restrict our search parameters to adjectives modifying the noun ass , regardless of whether other adjectives intervened, for example in expressions like silly old ass , which our query would have missed if they occurred in the BNC (they do not).

It should have become clear that the designs in Tables 7.1 and 7.3 are essentially variants of the general research design introduced in previous chapters and used as the foundation of defining corpus linguistics: it has two variables, POSITION 1 and POSITION 2, both of which have two values, namely WORD X VS. OTHER WORDS (or, in the case of differential collocates, WORD X VS. WORD Y). The aim is to determine whether the value WORD A is more frequent for POSITION 1 under the condition that WORD B occurs in POSITION 2 than under the condition that other words (or a particular other word) occur in POSITION 2.

7.1.2 Methodological issues in collocation research

We may occasionally be interested in an individual pair of collocates, such as silly ass , or in a small set of such pairs, such as all adjective-noun pairs with ass as the noun. However, it is much more likely that we will be interested in large sets of collocate pairs, such as all adjective-noun pairs or even all word pairs in a given corpus. This has a number of methodological consequences concerning the practicability, the statistical evaluation and the epistemological status of collocation research.

a. Practicability . In practical terms, the analysis of large numbers of potential collocations requires creating a large number of contingency tables and subjecting them to the χ 2 test or some other appropriate statistical test. This becomes implausibly time-consuming very quickly and thus needs to be automated in some way.

There are concordancing programs that offer some built-in statistical tests, but they typically restrict our options quite severely, both in terms of the tests they allow us to perform and in terms of the data on which the tests are performed. Anyone who decides to become involved in collocation research (or some of the large-scale lexical research areas described in the next chapter), should get acquainted at least with the simple options of automatizing statistical testing offered by spreadsheet applications. Better yet, they should invest a few weeks (or, in the worst case, months) to learn a scripting language like Perl, Python or R (the latter being a combination of statistical software and programming environment that is ideal for almost any task that we are likely to come across as corpus linguists).

b. Statistical evaluation . In statistical terms, the analysis of large numbers of potential collocations requires us to keep in mind that we are now performing multiple significance tests on the same set of data. This means that we must adjust our significance levels. Think back to the example of coin-flipping: the probability of getting a series of one head and nine tails is 0.009765. If we flip a coin ten times and get this result, we could thus reject the null hypothesis with a probability of error of 0.010744, i.e., around 1 percent (because we would have to add the probability of getting ten tails, 0.000976). This is well below the level required to claim statistical significance. However, if we perform one hundred series of ten coin-flips and one of these series consists of one head and nine tails (or ten tails), we could not reject the null hypothesis with the same confidence, as a probability of 0.010744 means that we would expect one such series to occur by chance. This is not a problem as long as we do not accord this one result out of a hundred any special importance. However, if we were to identify a set of 100 collocations with p -values of 0.001 in a corpus, we are potentially treating all of them as important, even though it is very probable that at least one of them reached this level of significance by chance.

To avoid this, we have to correct our levels of significance when performing multiple tests on the same set of data. As discussed in Section 6.6.1 above, the simplest way to do this is the Bonferroni correction, which consists in dividing the conventionally agreed-upon significance levels by the number of tests we are performing. As noted in Section 6.6.1, this is an extremely conservative correction that might make it quite difficult for any given collocation to reach significance.

Of course, the question is how important the role of p -values is in a design where our main aim is to identify collocates and order them in terms of their collocation strength. I will turn to this point presently, but before I do so, let us discuss the third of the three consequences of large-scale testing for collocation, the methodological one.

c. Epistemological considerations . We have, up to this point, presented a very narrow view of the scientific process based (in a general way) on the Popperian research cycle where we formulate a research hypothesis and then test it (either directly, by looking for counterexamples, or, more commonly, by attempting to reject the corresponding null hypothesis). This is called the deductive method. However, as briefly discussed in Chapter 3, there is an alternative approach to scientific research that does not start with a hypothesis, but rather with general questions like “Do relationships exist between the constructs in my data?” and “If so, what are those relationships?”. The research then consists in applying statistical procedures to large amounts of data and examining the results for interesting patterns. As electronic storage and computing power have become cheaper and more widely accessible, this approach – the exploratory or inductive approach – has become increasingly popular in all branches of science, particularly the social sciences. It would be surprising if corpus linguistics was an exception, and indeed, it is not. Especially the area of collocational research is typically exploratory.

In principle, there is nothing wrong with exploratory research – on the contrary, it would be unreasonable not to make use of the large amounts of language data and the vast computing power that has become available and accessible over the last thirty years. In fact, it is sometimes difficult to imagine a plausible hypothesis for collocational research projects. What hypothesis would we formulate before identifying all collocations in the LOB or some specialized corpus (e.g., a corpus of business correspondence, a corpus of flight-control communication or a corpus of learner language)? 4 Despite this, it is clear that the results of such a collocation analysis yield interesting data, both for practical purposes (building dictionaries or teaching materials for business English or aviation English, extracting terminology for the purpose of standardization, training natural-language processing systems) and for theoretical purposes (insights into the nature of situational language variation or even the nature of language in general).

But there is a danger, too: Most statistical procedures will produce some statistically significant result if we apply them to a large enough data set, and collocational methods certainly will. Unless we are interested exclusively in description, the crucial question is whether these results are meaningful. If we start with a hypothesis, we are restricted in our interpretation of the data by the need to relate our data to this hypothesis. If we do not start with a hypothesis, we can interpret our results without any restrictions, which, given the human propensity to see patterns everywhere, may lead to somewhat arbitrary post-hoc interpretations that could easily be changed, even reversed, if the results had been different and that therefore tell us very little about the phenomenon under investigation or language in general. Thus, it is probably a good idea to formulate at least some general expectations before doing a large-scale collocation analysis.

Even if we do start out with general expectations or even with a specific hypothesis, we will often discover additional facts about our phenomenon that go beyond what is relevant in the context of our original research question. For example, checking in the BNC Firth’s claim that the most frequent collocates of ass are silly , obstinate , stupid , awful and egregious and that young is “much more frequent” than old , we find that silly is indeed the most frequent adjectival collocate, but that obstinate , stupid and egregious do not occur at all, that awful occurs only once, and that young and old both occur twice. Instead, frequent adjectival collocates (ignoring second-placed wild , which exclusively refers to actual donkeys), are pompous and bad . Pompous does not really fit with the semantics that Firth’s adjectives suggest and could indicate that a semantic shift from ‘stupidity’ to ‘self-importance’ may have taken place between 1957 and 1991 (when the BNC was assembled).

This is, of course, a new hypothesis that can (and must) be investigated by comparing data from the 1950s and the 1990s. It has some initial plausibility in that the adjectives blithering , hypocritical , monocled and opinionated also co-occur with ass in the BNC but are not mentioned by Firth. However, it is crucial to treat this as a hypothesis rather than a result. The same goes for bad ass which suggests that the American sense of ass (‘bottom’) and/or the American adjective badass (which is often spelled as two separate words) may have begun to enter British English. In order to be tested, these ideas – and any ideas derived from an exploratory data analysis – have to be turned into testable hypotheses and the constructs involved have to be operationalized. Crucially, they must be tested on a new data set – if we were to circularly test them on the same data that they were derived from, we would obviously find them confirmed.

7.1.3 Effect sizes for collocations

As mentioned above, significance testing (while not without its uses) is not necessarily our primary concern when investigating collocations. Instead, researchers frequently need a way of assessing the strength of the association between two (or more) words, or, put differently, the effect size of their co-occurrence (recall from Chapter 6 that significance and effect size are not the same). A wide range of such association measures has been proposed and investigated. They are typically calculated on the basis of (some or all) the information contained in contingency tables like those in Tables 7.1 and 7.3 above.

Let us look at some of the most popular and/or most useful of these measures. I will represent the formulas with reference to the table in Table 7.5, i.e, O 11 means the observed frequency of the top left cell, E 11 its expected frequency, R 1 the first row total, C 2 the second column total, and so on. Note that second column would be labeled OTHER WORDS in the case of normal collocations, and WORD C in the case of differential collocations. The association measures can be applied to both kinds of design.

Table 7.5: A generic 2-by-2 table for collocation research

Now all we need is a good example to demonstrate the calculations. Let us use the adjective-noun sequence good example from the LOB corpus (but horse lovers need not fear, we will return to equine animals and their properties below).

Table 7.6: Co-occurrence of good and example in the LOB

Measures of collocation strength differ with respect to the data needed to calculate them, their computational intensiveness and, crucially, the quality of their results. In particular, many measures, notably the ones easy to calculate, have a problem with rare collocations, especially if the individual words of which they consist are also rare. After we have introduced the measures, we will therefore compare their performance with a particular focus on the way in which they deal (or fail to deal) with such rare events.

7.1.3.1 Chi-square

The first association measure is an old acquaintance: the chi-square statistic, which we used extensively in Chapter 6 and in Section 7.1.1 above. I will not demonstrate it again, but the chi-square value for Table 7.6 would be 378.95 (at 1 degree of freedom this means that p < 0.001, but we are not concerned with p -values here).

Recall that the chi-square test statistic is not an effect size, but that it needs to be divided by the table total to turn it into one. As long as we are deriving all our collocation data from the same corpus, this will not make a difference, since the table total will always be the same. However, this is not always the case. Where table sizes differ, we might consider using the phi value instead. I am not aware of any research using phi as an association measure, and in fact the chi-square statistic itself is not used widely either. This is because it has a serious problem: recall that it cannot be applied if more than 20 percent of the cells of the contingency table contain expected frequencies smaller than 5 (in the case of collocates, this means not even one out of the four cells of the 2-by-2 table). One reason for this is that it dramatically overestimates the effect size and significance of such events, and of rare events in general. Since collocations are often relatively rare events, this makes the chi-square statistic a bad choice as an association measure.

7.1.3.2 Mutual Information

Mutual information is one of the oldest collocation measures, frequently used in computational linguistics and often implemented in collocation software. It is given in (1) in a version based on Church & Hanks (1990): 5

Applying the formula to our table, we get the following:

In our case, we are looking at cases where WORD A and WORD B occur directly next to each other, i.e., the span size is 1. When looking at a larger span (which is often done in collocation research), the probability of encountering a particular collocate increases, because there are more slots that it could potentially occur in. The MI statistic can be adjusted for larger span sizes as follows (where S is the span size):

The mutual information measure suffers from the same problem as the χ 2 statistic: it overestimates the importance of rare events. Since it is still fairly widespread in collocational research, we may nevertheless need it in situations where we want to compare our own data to the results of published studies. However, note that there are versions of the MI measure that will give different results, so we need to make sure we are using the same version as the study we are comparing our results to. But unless there is a pressing reason, we should not use mutual information at all.

7.1.3.3 The log-likelihood ratio test

The G value of the log-likelihood ratio test is one of the most popular – perhaps the most popular – association measure in collocational research, found in many of the central studies in the field and often implemented in collocation software. The following is a frequently found form (Read & Cressie 1988: 134):

In order to calculate the G measure, we calculate for each cell the natural logarithm of the observed frequency divided by the expected frequency and multiply it by the observed frequency. We then add up the results for all four cells and multiply the result by two. Note that if the observed frequency of a given cell is zero, the expression O i / Ei will, of course, also be zero. Since the logarithm of zero is undefined, this would result in an error in the calculation. Thus, log(0) is simply defined as zero when applying the formula in (3).

Applying the formula in (3) to the data in Table 7.6, we get the following:

The G value has long been known to be more reliable than the χ 2 test when dealing with small samples and small expected frequencies (Read & Cressie 1988: 134ff). This led Dunning (1993) to propose it as an association measure specifically to avoid the overestimation of rare events that plagues the χ 2 test, mutual information and other measures.

7.1.3.4 Minimum Sensitivity

Minimum sensitivity was proposed by Pedersen (1998) as potentially useful measure especially for the identification of associations between content words:

We simply divide the observed frequency of a collocation by the frequency of the first word (R 1 ) and of the second word (C 1 ) and use the smaller of the two as the association measure. For the data in Table 7.6, this gives us the following:

In addition to being extremely simple to calculate, it has the advantage of ranging from zero (words never occur together) to 1 (words always occur together); it was also argued by Wiechmann (2008) to correlate best with reading time data when applied to combinations of words and grammatical constructions (see Chapter 8). However, it also tends to overestimate the importance of rare collocations.

7.1.3.5 Fisher’s exact test

isher’s exact test was already mentioned in passing in Chapter 6 as an alternative to the χ 2 test that calculates the probability of error directly by adding up the probability of the observed distribution and all distributions that deviate from the null hypothesis further in the same direction. Pedersen (1996) suggests using this p -value as a measure of association because it does not make any assumptions about normality and is even better at dealing with rare events than G. Stefanowitsch & Gries (2003: 238–239) add that it has the advantage of taking into account both the magnitude of the deviation from the expected frequencies and the sample size.

There are some practical disadvantages to Fisher’s exact test. First, it is computationally expensive – it cannot be calculated manually, except for very small tables, because it involves computing factorials, which become very large very quickly. For completeness’ sake, here is (one version of) the formula:

Obviously, it is not feasible to apply this formula directly to the data in Table 7.6, because we cannot realistically calculate the factorials for 236 or 836, let alone 1 011 904. But if we could, we would find that the p -value for Table 7.6 is 0.000000000001188.

Spreadsheet applications do not usually offer Fisher’s exact test, but all major statistics applications do. However, typically, the exact p -value is not reported beyond the limit of a certain number of decimal places. This means that there is often no way of ranking the most strongly associated collocates, because their p -values are smaller than this limit. For example, there are more than 100 collocates in the LOB corpus with a Fisher’s exact p -value that is smaller than the smallest value that a standard-issue computer chip is capable of calculating, and more than 5000 collocates that have ​​​​​​​p -values that are smaller than what the standard implementation of Fisher’s exact test in the statistical software package R will deliver. Since in research on collocations we often need to rank collocations in terms of their strength, this may become a problem.

7.1.3.6 A comparison of association measures

Let us see how the association measures compare using a data set of 20 potential collocations. Inspired by Firth’s silly ass , they are all combinations of adjectives with equine animals. Table 7.7 shows the combinations and their frequencies in the BNC sorted by their raw frequency of occurrence (adjectives and nouns are shown in small caps here to stress that they are values of the variables Word A and Word B, but I will generally show them in italics in the remainder of the book in line with linguistic tradition).

All combinations are perfectly normal, grammatical adjective-noun pairs, meaningful not only in the specific context of their actual occurrence. However, I have selected them in such a way that they differ with respect to their status as potential collocations (in the sense of typical combinations of words). Some are compounds or compound like combinations ( rocking horse , Trojan horse , and, in specialist discourse, common zebra ). Some are the kind of semi-idiomatic combinations that Firth had in mind ( silly ass , pompous ass ). Some are very conventional combinations of nouns with an adjective denoting a property specific to that noun ( prancing horse , braying donkey , galloping horse – the first of these being a conventional way of referring to the Ferrari brand mark logo). Some only give the appearance of semi-idiomatic combinations ( jumped-up jackass , actually an unconventional variant of jumped-up jack-in-office ; dumb-fuck donkey , actually an extremely rare phrase that occurs only once in the documented history of English, namely in the book Trail of the Octopus: From Beirut to Lockerbie – Inside the DIA and that probably sounds like an idiom because of the alliteration and the semantic relationship to silly ass ; and monocled ass , which brings to mind pompous ass but is actually not a very conventional combination). Finally, there are a number of fully compositional combinations that make sense but do not have any special status ( caparisoned mule , new horse , old donkey , young zebra , large mule , female hinny , extinct quagga ).

Table 7.7: Some collocates of the form [ADJ N equine ] (BNC)

In addition, I have selected them to represent different types of frequency relations: some of them are (relatively) frequent, some of them very rare, for some of them the either the adjective or the noun is generally quite frequent, and for some of them neither of the two is frequent.

Table 7.8 shows the ranking of these twenty collocations by the five association measures discussed above. Simplifying somewhat, a good association measure should rank the conventionalized combinations highest ( rocking horse, Trojan horse, silly ass, pompous ass, prancing horse, braying donkey, galloping horse ), the distinctive sounding but non-conventionalized combinations somewhere in the middle ( jumped-up jackass, dumb-fuck donkey, old ass, monocled ass ) and the compositional combinations lowest ( common zebra, jumped-up jackass, dumb-fuck donkey, old ass, monocled ass ). Common zebra is difficult to predict – it is a conventionalized expression, but not in the general language.

Table 7.8: Comparison of selected association measures for collocates of the form [ADJ N equine ] (BNC)

All association measures fare quite well, generally speaking, with respect to the compositional expressions – these tend to occur in the lower third of all lists. Where there are exceptions, the χ 2 statistic, mutual information and minimum sensitivity rank rare cases higher than they should (e.g. caparisoned mule , extinct quagga ), while the G and the p -value of Fisher’s exact test rank frequent cases higher (e.g. galloping horse ).

With respect to the non-compositional cases, χ 2 and mutual information are quite bad, overestimating rare combinations like jumped-up jackass, dumb-fuck donkey and monocled ass , while listing some of the clear cases of collocations much further down the list ( silly ass , and, in the case of MI, rocking horse ). Minimum sensitivity is much better, ranking most of the conventionalized cases in the top half of the list and the non-conventionalized ones further down (with the exception of jumped-up jackass , where both the individual words and their combination are very rare). The G and the Fisher p -value fare best (with no differences in their ranking of the expressions), listing the conventionalized cases at the top and the distinctive but non-conventionalized cases in the middle.

To demonstrate the problems that very rare events can cause (especially those where both the combination and each of the two words in isolation are very rare), imagine someone had used the phrase tomfool onager once in the BNC. Since neither the adjective tomfool (a synonym of silly) nor the noun onager (the name of the donkey sub-genus Equus hemionus, also known as Asiatic or Asian wild ass ) occur in the BNC anywhere else, this would give us the distribution in Table 7.9.

Table 7.9: Fictive occurrence of tomfool onager in the BNC

Applying the formulas discussed above to this table gives us a χ 2 value of 98 364 000, an MI value of 26.55 and a minimum sensitivity value of 1, placing this (hypothetical) one-off combination at the top of the respective rankings by a wide margin. Again, the log-likelihood ratio test and Fisher’s exact test are much better, putting in eighth place on both lists ( G = 36.81, p exact = 1.02 × 10 −8 ).

Although the example is hypothetical, the problem is not. It uncovers a mathematical weakness of many commonly used association measures. From an empirical perspective, this would not necessarily be a problem, if cases like that in Table 7.9 were rare in linguistic corpora. However, they are not. The LOB corpus, for example, contains almost one thousand such cases, including some legitimate collocation candidates (like herbal brews, casus belli or sub-tropical climates ), but mostly compositional combinations ( ungraceful typography, turbaned headdress, songs-of-Britain medley ), snippets of foreign languages ( freie Blicke, l’arbre rouge, palomita blanca ) and other things that are quite clearly not what we are looking for in collocation research. All of these will occur at the top of any collocate list created using statistics like χ 2 , mutual information and minimum sensitivity. In large corpora, which are impossible to check for orthographical errors and/or errors introduced by tokenization, this list will also include hundreds of such errors (whose frequency of occurrence is low precisely because they are errors).

To sum up, when doing collocational research, we should use the best association measures available. For the time being, this is the p value of Fisher’s exact test (if we have the means to calculate it), or G (if we don’t, or if we prefer using a widely-accepted association measure). We will use G through much of the remainder of this book whenever dealing with collocations or collocation-like phenomena.

1 Note that we are using the corpus size as the table total – strictly speaking, we should be using the total number of two-word sequences (bigrams) in the corpus, which will be lower: The last word in each file of our corpus will not have a word following it, so we would have to subtract the last word of each file – i.e., the number of files in our corpus – from the total. This is unlikely to make much of a difference in most cases, but the shorter the texts in our corpus are, the larger the difference will be. For example, in a corpus of tweets, which, at the time of writing, are limited to 280 characters, it might be better to correct the total number of bigrams in the way described.

2 Gries (2003b) and Gries & Stefanowitsch (2004) use the term distinctive collocate , which has been taken up by some authors; however, many other authors use the term distinctive collocate much more broadly to refer to characteristic collocates of a word.

3 Note that such word-class specific collocations are sometimes referred to as colligations, although the term colligation usually refers to the co-occurrence of a word in the context of particular word classes, which is not the same.

4 Of course we are making the implicit assumption that there will be collocates – in a sense, this is a hypothesis, since we could conceive of models of language that would not predict their existence (we might argue, for example, that at least some versions of generative grammar constitute such models). However, even if we accept this as a hypothesis, it is typically not the one we are interested in this kind of study.

5 A logarithm with a base b of a given number x is the power to which b must be raised to produce x , so, for example, log 10 (2) = 0.30103, because 10 0.30103 = 2. Most calculators offer at the very least a choice between the natural logarithm, where the base is the number e (approx. 2.7183) and the common logarithm, where the base is the number 10; many calculators and all major spreadsheet programs offer logarithms with any base. In the formula in (1), we need the logarithm with base 2; if this is not available, we can use the natural logarithm and divide the result by the natural logarithm of 2:

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Nominal Collocations in Scientific English: A Frame-Semantic Approach

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• First Online: 18 September 2019
• Cite this conference paper

• Eva Lucía Jiménez-Navarro 10  

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11755))

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• International Conference on Computational and Corpus-Based Phraseology

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In the last two decades, interest in the role played by phraseological units in the discourse of science has grown. Linguists have agreed that collocational frameworks help to structure the text and display a more restricted set of senses when used in this context. This paper aims at contributing to the study of collocations in the research article (RA). To this end, more than 400 collocations are analyzed in terms of Fillmore’s Frame Semantics theory. Our methodology is corpus-based and explores adjective + noun open domain collocations extracted from the British National Corpus (BNC) in a specific corpus of more than three million words. The findings suggest that these collocations convey specific meanings when they are used in this genre, being the headword the element evoking the semantic frame of the combination and the collocate expressing a feature of the former. The frames evoked reflect the semantics of science and their combination shows the anatomy of the RA.

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Jiménez-Navarro, E.L. (2019). Nominal Collocations in Scientific English: A Frame-Semantic Approach. In: Corpas Pastor, G., Mitkov, R. (eds) Computational and Corpus-Based Phraseology. EUROPHRAS 2019. Lecture Notes in Computer Science(), vol 11755. Springer, Cham. https://doi.org/10.1007/978-3-030-30135-4_14

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Why the Pandemic Probably Started in a Lab, in 5 Key Points

By Alina Chan

Dr. Chan is a molecular biologist at the Broad Institute of M.I.T. and Harvard, and a co-author of “Viral: The Search for the Origin of Covid-19.”

This article has been updated to reflect news developments.

On Monday, Dr. Anthony Fauci returned to the halls of Congress and testified before the House subcommittee investigating the Covid-19 pandemic. He was questioned about several topics related to the government’s handling of Covid-19, including how the National Institute of Allergy and Infectious Diseases, which he directed until retiring in 2022, supported risky virus work at a Chinese institute whose research may have caused the pandemic.

For more than four years, reflexive partisan politics have derailed the search for the truth about a catastrophe that has touched us all. It has been estimated that at least 25 million people around the world have died because of Covid-19, with over a million of those deaths in the United States.

Although how the pandemic started has been hotly debated, a growing volume of evidence — gleaned from public records released under the Freedom of Information Act, digital sleuthing through online databases, scientific papers analyzing the virus and its spread, and leaks from within the U.S. government — suggests that the pandemic most likely occurred because a virus escaped from a research lab in Wuhan, China. If so, it would be the most costly accident in the history of science.

Here’s what we now know:

1 The SARS-like virus that caused the pandemic emerged in Wuhan, the city where the world’s foremost research lab for SARS-like viruses is located.

• At the Wuhan Institute of Virology, a team of scientists had been hunting for SARS-like viruses for over a decade, led by Shi Zhengli.
• Their research showed that the viruses most similar to SARS‑CoV‑2, the virus that caused the pandemic, circulate in bats that live r oughly 1,000 miles away from Wuhan. Scientists from Dr. Shi’s team traveled repeatedly to Yunnan province to collect these viruses and had expanded their search to Southeast Asia. Bats in other parts of China have not been found to carry viruses that are as closely related to SARS-CoV-2.

The closest known relatives to SARS-CoV-2 were found in southwestern China and in Laos.

Large cities

Mine in Yunnan province

Cave in Laos

South China Sea

The closest known relatives to SARS-CoV-2

were found in southwestern China and in Laos.

philippines

The closest known relatives to SARS-CoV-2 were found

in southwestern China and Laos.

Sources: Sarah Temmam et al., Nature; SimpleMaps

Note: Cities shown have a population of at least 200,000.

There are hundreds of large cities in China and Southeast Asia.

There are hundreds of large cities in China

and Southeast Asia.

The pandemic started roughly 1,000 miles away, in Wuhan, home to the world’s foremost SARS-like virus research lab.

The pandemic started roughly 1,000 miles away,

in Wuhan, home to the world’s foremost SARS-like virus research lab.

The pandemic started roughly 1,000 miles away, in Wuhan,

home to the world’s foremost SARS-like virus research lab.

• Even at hot spots where these viruses exist naturally near the cave bats of southwestern China and Southeast Asia, the scientists argued, as recently as 2019 , that bat coronavirus spillover into humans is rare .
• When the Covid-19 outbreak was detected, Dr. Shi initially wondered if the novel coronavirus had come from her laboratory , saying she had never expected such an outbreak to occur in Wuhan.
• The SARS‑CoV‑2 virus is exceptionally contagious and can jump from species to species like wildfire . Yet it left no known trace of infection at its source or anywhere along what would have been a thousand-mile journey before emerging in Wuhan.

2 The year before the outbreak, the Wuhan institute, working with U.S. partners, had proposed creating viruses with SARS‑CoV‑2’s defining feature.

• Dr. Shi’s group was fascinated by how coronaviruses jump from species to species. To find viruses, they took samples from bats and other animals , as well as from sick people living near animals carrying these viruses or associated with the wildlife trade. Much of this work was conducted in partnership with the EcoHealth Alliance, a U.S.-based scientific organization that, since 2002, has been awarded over $80 million in federal funding to research the risks of emerging infectious diseases.
• The laboratory pursued risky research that resulted in viruses becoming more infectious : Coronaviruses were grown from samples from infected animals and genetically reconstructed and recombined to create new viruses unknown in nature. These new viruses were passed through cells from bats, pigs, primates and humans and were used to infect civets and humanized mice (mice modified with human genes). In essence, this process forced these viruses to adapt to new host species, and the viruses with mutations that allowed them to thrive emerged as victors.
• By 2019, Dr. Shi’s group had published a database describing more than 22,000 collected wildlife samples. But external access was shut off in the fall of 2019, and the database was not shared with American collaborators even after the pandemic started , when such a rich virus collection would have been most useful in tracking the origin of SARS‑CoV‑2. It remains unclear whether the Wuhan institute possessed a precursor of the pandemic virus.
• In 2021, The Intercept published a leaked 2018 grant proposal for a research project named Defuse , which had been written as a collaboration between EcoHealth, the Wuhan institute and Ralph Baric at the University of North Carolina, who had been on the cutting edge of coronavirus research for years. The proposal described plans to create viruses strikingly similar to SARS‑CoV‑2.
• Coronaviruses bear their name because their surface is studded with protein spikes, like a spiky crown, which they use to enter animal cells. T he Defuse project proposed to search for and create SARS-like viruses carrying spikes with a unique feature: a furin cleavage site — the same feature that enhances SARS‑CoV‑2’s infectiousness in humans, making it capable of causing a pandemic. Defuse was never funded by the United States . However, in his testimony on Monday, Dr. Fauci explained that the Wuhan institute would not need to rely on U.S. funding to pursue research independently.

The Wuhan lab ran risky experiments to learn about how SARS-like viruses might infect humans.

1. Collect SARS-like viruses from bats and other wild animals, as well as from people exposed to them.

2. Identify high-risk viruses by screening for spike proteins that facilitate infection of human cells.

2. Identify high-risk viruses by screening for spike proteins that facilitate infection of

human cells.

In Defuse, the scientists proposed to add a furin cleavage site to the spike protein.

3. Create new coronaviruses by inserting spike proteins or other features that could make the viruses more infectious in humans.

4. Infect human cells, civets and humanized mice with the new coronaviruses, to determine how dangerous they might be.

• While it’s possible that the furin cleavage site could have evolved naturally (as seen in some distantly related coronaviruses), out of the hundreds of SARS-like viruses cataloged by scientists, SARS‑CoV‑2 is the only one known to possess a furin cleavage site in its spike. And the genetic data suggest that the virus had only recently gained the furin cleavage site before it started the pandemic.
• Ultimately, a never-before-seen SARS-like virus with a newly introduced furin cleavage site, matching the description in the Wuhan institute’s Defuse proposal, caused an outbreak in Wuhan less than two years after the proposal was drafted.
• When the Wuhan scientists published their seminal paper about Covid-19 as the pandemic roared to life in 2020, they did not mention the virus’s furin cleavage site — a feature they should have been on the lookout for, according to their own grant proposal, and a feature quickly recognized by other scientists.
• Worse still, as the pandemic raged, their American collaborators failed to publicly reveal the existence of the Defuse proposal. The president of EcoHealth, Peter Daszak, recently admitted to Congress that he doesn’t know about virus samples collected by the Wuhan institute after 2015 and never asked the lab’s scientists if they had started the work described in Defuse. In May, citing failures in EcoHealth’s monitoring of risky experiments conducted at the Wuhan lab, the Biden administration suspended all federal funding for the organization and Dr. Daszak, and initiated proceedings to bar them from receiving future grants. In his testimony on Monday, Dr. Fauci said that he supported the decision to suspend and bar EcoHealth.
• Separately, Dr. Baric described the competitive dynamic between his research group and the institute when he told Congress that the Wuhan scientists would probably not have shared their most interesting newly discovered viruses with him . Documents and email correspondence between the institute and Dr. Baric are still being withheld from the public while their release is fiercely contested in litigation.
• In the end, American partners very likely knew of only a fraction of the research done in Wuhan. According to U.S. intelligence sources, some of the institute’s virus research was classified or conducted with or on behalf of the Chinese military . In the congressional hearing on Monday, Dr. Fauci repeatedly acknowledged the lack of visibility into experiments conducted at the Wuhan institute, saying, “None of us can know everything that’s going on in China, or in Wuhan, or what have you. And that’s the reason why — I say today, and I’ve said at the T.I.,” referring to his transcribed interview with the subcommittee, “I keep an open mind as to what the origin is.”

3 The Wuhan lab pursued this type of work under low biosafety conditions that could not have contained an airborne virus as infectious as SARS‑CoV‑2.

• Labs working with live viruses generally operate at one of four biosafety levels (known in ascending order of stringency as BSL-1, 2, 3 and 4) that describe the work practices that are considered sufficiently safe depending on the characteristics of each pathogen. The Wuhan institute’s scientists worked with SARS-like viruses under inappropriately low biosafety conditions .

In the United States, virologists generally use stricter Biosafety Level 3 protocols when working with SARS-like viruses.

Biosafety cabinets prevent

viral particles from escaping.

Viral particles

Personal respirators provide

a second layer of defense against breathing in the virus.

DIRECT CONTACT

Gloves prevent skin contact.

Disposable wraparound

gowns cover much of the rest of the body.

Personal respirators provide a second layer of defense against breathing in the virus.

Disposable wraparound gowns

cover much of the rest of the body.

Note: ​​Biosafety levels are not internationally standardized, and some countries use more permissive protocols than others.

The Wuhan lab had been regularly working with SARS-like viruses under Biosafety Level 2 conditions, which could not prevent a highly infectious virus like SARS-CoV-2 from escaping.

Some work is done in the open air, and masks are not required.

Less protective equipment provides more opportunities

for contamination.

Some work is done in the open air,

and masks are not required.

Less protective equipment provides more opportunities for contamination.

• In one experiment, Dr. Shi’s group genetically engineered an unexpectedly deadly SARS-like virus (not closely related to SARS‑CoV‑2) that exhibited a 10,000-fold increase in the quantity of virus in the lungs and brains of humanized mice . Wuhan institute scientists handled these live viruses at low biosafet y levels , including BSL-2.
• Even the much more stringent containment at BSL-3 cannot fully prevent SARS‑CoV‑2 from escaping . Two years into the pandemic, the virus infected a scientist in a BSL-3 laboratory in Taiwan, which was, at the time, a zero-Covid country. The scientist had been vaccinated and was tested only after losing the sense of smell. By then, more than 100 close contacts had been exposed. Human error is a source of exposure even at the highest biosafety levels , and the risks are much greater for scientists working with infectious pathogens at low biosafety.
• An early draft of the Defuse proposal stated that the Wuhan lab would do their virus work at BSL-2 to make it “highly cost-effective.” Dr. Baric added a note to the draft highlighting the importance of using BSL-3 to contain SARS-like viruses that could infect human cells, writing that “U.S. researchers will likely freak out.” Years later, after SARS‑CoV‑2 had killed millions, Dr. Baric wrote to Dr. Daszak : “I have no doubt that they followed state determined rules and did the work under BSL-2. Yes China has the right to set their own policy. You believe this was appropriate containment if you want but don’t expect me to believe it. Moreover, don’t insult my intelligence by trying to feed me this load of BS.”
• SARS‑CoV‑2 is a stealthy virus that transmits effectively through the air, causes a range of symptoms similar to those of other common respiratory diseases and can be spread by infected people before symptoms even appear. If the virus had escaped from a BSL-2 laboratory in 2019, the leak most likely would have gone undetected until too late.
• One alarming detail — leaked to The Wall Street Journal and confirmed by current and former U.S. government officials — is that scientists on Dr. Shi’s team fell ill with Covid-like symptoms in the fall of 2019 . One of the scientists had been named in the Defuse proposal as the person in charge of virus discovery work. The scientists denied having been sick .

4 The hypothesis that Covid-19 came from an animal at the Huanan Seafood Market in Wuhan is not supported by strong evidence.

• In December 2019, Chinese investigators assumed the outbreak had started at a centrally located market frequented by thousands of visitors daily. This bias in their search for early cases meant that cases unlinked to or located far away from the market would very likely have been missed. To make things worse, the Chinese authorities blocked the reporting of early cases not linked to the market and, claiming biosafety precautions, ordered the destruction of patient samples on January 3, 2020, making it nearly impossible to see the complete picture of the earliest Covid-19 cases. Information about dozens of early cases from November and December 2019 remains inaccessible.
• A pair of papers published in Science in 2022 made the best case for SARS‑CoV‑2 having emerged naturally from human-animal contact at the Wuhan market by focusing on a map of the early cases and asserting that the virus had jumped from animals into humans twice at the market in 2019. More recently, the two papers have been countered by other virologists and scientists who convincingly demonstrate that the available market evidence does not distinguish between a human superspreader event and a natural spillover at the market.
• Furthermore, the existing genetic and early case data show that all known Covid-19 cases probably stem from a single introduction of SARS‑CoV‑2 into people, and the outbreak at the Wuhan market probably happened after the virus had already been circulating in humans.

An analysis of SARS-CoV-2’s evolutionary tree shows how the virus evolved as it started to spread through humans.

SARS-COV-2 Viruses closest

to bat coronaviruses

more mutations

Source: Lv et al., Virus Evolution (2024) , as reproduced by Jesse Bloom

The viruses that infected people linked to the market were most likely not the earliest form of the virus that started the pandemic.

• Not a single infected animal has ever been confirmed at the market or in its supply chain. Without good evidence that the pandemic started at the Huanan Seafood Market, the fact that the virus emerged in Wuhan points squarely at its unique SARS-like virus laboratory.

5 Key evidence that would be expected if the virus had emerged from the wildlife trade is still missing.

In previous outbreaks of coronaviruses, scientists were able to demonstrate natural origin by collecting multiple pieces of evidence linking infected humans to infected animals.

Infected animals

Earliest known

cases exposed to

live animals

Antibody evidence

of animals and

animal traders having

been infected

Ancestral variants

of the virus found in

Documented trade

of host animals

between the area

where bats carry

closely related viruses

and the outbreak site

Infected animals found

Earliest known cases exposed to live animals

Antibody evidence of animals and animal

traders having been infected

Ancestral variants of the virus found in animals

Documented trade of host animals

between the area where bats carry closely

related viruses and the outbreak site

For SARS-CoV-2, these same key pieces of evidence are still missing , more than four years after the virus emerged.

For SARS-CoV-2, these same key pieces of evidence are still missing ,

more than four years after the virus emerged.

• Despite the intense search trained on the animal trade and people linked to the market, investigators have not reported finding any animals infected with SARS‑CoV‑2 that had not been infected by humans. Yet, infected animal sources and other connective pieces of evidence were found for the earlier SARS and MERS outbreaks as quickly as within a few days, despite the less advanced viral forensic technologies of two decades ago.
• Even though Wuhan is the home base of virus hunters with world-leading expertise in tracking novel SARS-like viruses, investigators have either failed to collect or report key evidence that would be expected if Covid-19 emerged from the wildlife trade . For example, investigators have not determined that the earliest known cases had exposure to intermediate host animals before falling ill. No antibody evidence shows that animal traders in Wuhan are regularly exposed to SARS-like viruses, as would be expected in such situations.
• With today’s technology, scientists can detect how respiratory viruses — including SARS, MERS and the flu — circulate in animals while making repeated attempts to jump across species . Thankfully, these variants usually fail to transmit well after crossing over to a new species and tend to die off after a small number of infections. In contrast, virologists and other scientists agree that SARS‑CoV‑2 required little to no adaptation to spread rapidly in humans and other animals . The virus appears to have succeeded in causing a pandemic upon its only detected jump into humans.

The pandemic could have been caused by any of hundreds of virus species, at any of tens of thousands of wildlife markets, in any of thousands of cities, and in any year. But it was a SARS-like coronavirus with a unique furin cleavage site that emerged in Wuhan, less than two years after scientists, sometimes working under inadequate biosafety conditions, proposed collecting and creating viruses of that same design.

While several natural spillover scenarios remain plausible, and we still don’t know enough about the full extent of virus research conducted at the Wuhan institute by Dr. Shi’s team and other researchers, a laboratory accident is the most parsimonious explanation of how the pandemic began.

Given what we now know, investigators should follow their strongest leads and subpoena all exchanges between the Wuhan scientists and their international partners, including unpublished research proposals, manuscripts, data and commercial orders. In particular, exchanges from 2018 and 2019 — the critical two years before the emergence of Covid-19 — are very likely to be illuminating (and require no cooperation from the Chinese government to acquire), yet they remain beyond the public’s view more than four years after the pandemic began.

Whether the pandemic started on a lab bench or in a market stall, it is undeniable that U.S. federal funding helped to build an unprecedented collection of SARS-like viruses at the Wuhan institute, as well as contributing to research that enhanced them . Advocates and funders of the institute’s research, including Dr. Fauci, should cooperate with the investigation to help identify and close the loopholes that allowed such dangerous work to occur. The world must not continue to bear the intolerable risks of research with the potential to cause pandemics .

A successful investigation of the pandemic’s root cause would have the power to break a decades-long scientific impasse on pathogen research safety, determining how governments will spend billions of dollars to prevent future pandemics. A credible investigation would also deter future acts of negligence and deceit by demonstrating that it is indeed possible to be held accountable for causing a viral pandemic. Last but not least, people of all nations need to see their leaders — and especially, their scientists — heading the charge to find out what caused this world-shaking event. Restoring public trust in science and government leadership requires it.

A thorough investigation by the U.S. government could unearth more evidence while spurring whistleblowers to find their courage and seek their moment of opportunity. It would also show the world that U.S. leaders and scientists are not afraid of what the truth behind the pandemic may be.

More on how the pandemic may have started

Where Did the Coronavirus Come From? What We Already Know Is Troubling.

Even if the coronavirus did not emerge from a lab, the groundwork for a potential disaster had been laid for years, and learning its lessons is essential to preventing others.

By Zeynep Tufekci

Why Does Bad Science on Covid’s Origin Get Hyped?

If the raccoon dog was a smoking gun, it fired blanks.

By David Wallace-Wells

A Plea for Making Virus Research Safer

A way forward for lab safety.

By Jesse Bloom

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

Follow the New York Times Opinion section on Facebook , Instagram , TikTok , WhatsApp , X and Threads .

Alina Chan ( @ayjchan ) is a molecular biologist at the Broad Institute of M.I.T. and Harvard, and a co-author of “ Viral : The Search for the Origin of Covid-19.” She was a member of the Pathogens Project , which the Bulletin of the Atomic Scientists organized to generate new thinking on responsible, high-risk pathogen research.

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Flutter of a plate at high supersonic speeds.

1. Introduction

2. general formulation, 3. piston theory approximation, 4. infinite strip plate, 5. rectangular plate, 6. analytical solution, 7. results and discussion, 7.1. infinite strip, 7.2. rectangular plate, 8. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.

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Sezgin, A.; Durak, B.; Sayın, A.; Yildiz, H.; Ozer, H.O.; Sakman, L.E.; Kapkin, S.; Uzal, E. Flutter of a Plate at High Supersonic Speeds. Appl. Sci. 2024 , 14 , 4892. https://doi.org/10.3390/app14114892

Sezgin A, Durak B, Sayın A, Yildiz H, Ozer HO, Sakman LE, Kapkin S, Uzal E. Flutter of a Plate at High Supersonic Speeds. Applied Sciences . 2024; 14(11):4892. https://doi.org/10.3390/app14114892

Sezgin, Aziz, Birkan Durak, Alaattin Sayın, Huseyin Yildiz, Hasan Omur Ozer, Lutfi Emir Sakman, Sule Kapkin, and Erol Uzal. 2024. "Flutter of a Plate at High Supersonic Speeds" Applied Sciences 14, no. 11: 4892. https://doi.org/10.3390/app14114892

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