Increasing Politicization and Homogeneity in Scientific Funding: An Analysis of NSF Grants, 1990-2020
CSPI Report No. 4
The National Science Foundation (NSF) is the main governmental scientific grant distributing body in the United States, with an annual budget of over $8 billion.
This report uses natural language processing to analyze the abstracts of successful grants from 1990 to 2020 in the seven fields of Biological Sciences, Computer & Information Science & Engineering, Education & Human Resources, Engineering, Geosciences, Mathematical & Physical Sciences, and Social, Behavioral & Economic Sciences.
The frequency of documents containing highly politicized terms has been increasing consistently over the last three decades. As of 2020, 30.4% of all grants had one of the following politicized terms: “equity,” “diversity,” “inclusion,” “gender,” “marginalize,” “underrepresented,” or “disparity.” This is up from 2.9% in 1990. The most politicized field is Education & Human Resources (53.8% in 2020, up from 4.3% in 1990). The least are Mathematical & Physical Sciences (22.6%, up from 0.9%) and Computer & Information Science & Engineering (24.9%, up from 1.5%), although even they are significantly more politicized than any field was in 1990.
At the same time, abstracts in most directorates have been becoming more similar to each other over time. This arguably shows that there is less diversity in the kinds of ideas that are getting funded. This effect is particularly strong in the last few years, but the trend is clear over the last three decades when a technique based on word similarity, rather than the matching of exact terms, is used.
Taken together, the results imply that there has been a politicization of scientific funding in the US in recent years and a decrease in the diversity of ideas supported, indicating a possible decline in the quality of research and the potential for decreased trust towards scientific institutions among the general public.
Code and data for replication are available here.
Richard Feynman introduced a concept he called “cargo cult science” during a commencement speech at Caltech in 1974.In the Second World War, Allied and Japanese airbases sprung up on islands in the South Pacific that were home to pre-industrial cultures that previously had little contact with the modern world. The soldiers on many of these bases would trade manufactured clothing, medicine, canned food, and other goods with the natives, most of which arrived by airdrop. After the war ended and the soldiers left, the native populations on some of the islands began to create replicas of things like airstrips, airplanes, and flight control towers. They even made mock radios and headphones out of coconuts and straw. The natives believed that by recreating the conditions under which the airplanes came and dropped goods, they could get the airdrops to resume.
There were entire areas of academic study that Feynman called “cargo cult science” – mostly in the fields of social science and education. These areas of inquiry see the success that the scientific method delivers in disciplines like physics, chemistry, and medicine, and produce superficial replicas of scientific practices. They miss something essential, however, and as Feynman says, “the planes never land.” So, what is this missing element in “cargo cult science?” It is “a kind of scientific integrity, a principle of scientific thought that corresponds to a kind of utter honesty.” Science requires a willingness to relentlessly assail one’s assumptions and a capacity to bend over backwards to try and disprove ideas even if one passionately wants to believe they are true. I would love to be able to revisit this critique of Feynman’s and have a good laugh at how strange and backwards academic institutions and scientific agencies must have been in the 1970s, but unfortunately, this critique feels as salient today as it must have when Feynman first expressed it almost 60 years ago.
Ideally, the “cargo cult sciences” should start to recede as their practitioners start to notice that “the planes aren't landing.” What if, however, we have in place institutions that have degraded to a point where they subsidize and reward practices that are not actual science, but a kind of science-like interpretive dance? Patrick Collison and Michael Nielsen find that there has been a precipitous increase in the number of science publications, PhD students graduating in STEM fields, and government spending on National Institute of Health (NIH) and National Science Foundation (NSF) grants since the 1960s.Their same work, however, shows that when scientists were surveyed about the importance of Nobel Prize winning discoveries between the 1920s and 1980s, results indicated anywhere from a decline to a general stagnation in the impact of science over that time period – nothing anywhere near the output we might expect given the tremendous amount of time and energy now invested. One theory posited by Collison, as well as economist Tyler Cowen and others, is that science might just be getting harder; we have gotten to all the low-hanging fruit and now are in the territory of diminishing returns.
Science may be getting harder, but it seems unlikely that this is the sole, or perhaps even largest, cause of decreasing productivity. In addition to the previously mentioned increase in funding to scientific research, the costs of many of the important inputs to research – such as computing power, gene sequencing, and various types of lab equipment – have been declining exponentially. With a rise in funding and a decline in the costs of many inputs, we might expect to be able to generate increased scientific output despite potentially increasing difficulty.
Although a fuller explanation for technological and scientific stagnation is beyond the scope of this work, here I analyze the abstracts of successful NSF applications and find two reasons to believe that something has gone wrong with the culture of science, particularly in the last few decades. The first of these is increasing politicization. If paying lip service to fashionable political ideas becomes an important criterion for successful grant applications, this will certainly detract from the importance of other more vital criteria – namely those related to the quality and importance of the proposed research. When the process of deciding what research projects get funded comes to be based on a political litmus test, the scientific endeavor suffers. Additionally, the more that scientific institutions come to be viewed as conduits for promulgating ideology, the less capable they will be of swaying public opinion on important issues. We may be starting to see the harmful effects of this process in the current epistemic crisis regarding public health. The growing view of science as a vehicle for activism detracts from its more vital role of being a dispassionate referee that adjudicates the validity of empirical claims.
The second major result in this work is the constriction of the space of ideas within NSF award abstracts. The number of NSF awards given and the total amount of taxpayer money spent by the NSF have increased consistently since 1990, and yet this work provides evidence suggesting that the breadth of ideas within NSF award abstracts has been contracting. In different contexts, bureaucracies can become positive feedback chambers reinforcing and amplifying favored ideas while excluding others. Recent work by Johan Chu and James Evans supports this view by showing that the larger a scientific field becomes, the more it tends to stagnate, with more reliance on established works in citation patterns and fewer fundamental breakthroughs.The NSF is ostensibly an organization meant to stimulate scientific progress for the benefit of the nation, but the way in which it has become entangled with academia and established institutions may make it seem more like a professional guild representing the interests of its members. Such factors could explain the stagnation we see in the ability of the NSF to identify and support novelty.
The NSF, an independent federal agency, has a stated mission “to promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense.”It has an annual budget of around $8.5 billion and funds approximately a quarter of all federally funded basic research at colleges and universities in the US. The NSF provides public access to archives of awards granted in every fiscal year since 1967. The data available in these archives do not include the full proposal texts, which are often lengthy and detailed documents, but only the abstracts of the proposals, which are generally only several hundred words in length. Additionally, all awards prior to 1986 do not have abstracts, and most awards do not have abstracts until 1988. Given these limitations of the data, this work only analyzes awards from 1990-2020. Other information provided in these archives for each grant are the identities and institutions of the primary investigators, the award amount, the title of the proposal, and the NSF directorate that granted the award. There are seven directorates that are responsible for dispersing the majority of NSF funding: Biological Sciences; Computer & Information Science & Engineering; Education & Human Resources; Engineering; Geosciences; Mathematical & Physical Sciences; and Social, Behavioral & Economic Sciences. All analysis conducted in this work is broken up by these seven directorates.
This section intends only to explain the processes used in analyzing the NSF award data at a higher level. These processes are explained in greater depth in Appendix IV. Additionally, the code used for the analyses in this work is publicly available to facilitate easy replication of the results presented here.
This method simply counts the number of documents containing some form of a given term. For example, for “inclusion” terms we look for documents containing any of the following forms: “inclusive,” “inclusivity,” and “inclusion.” Even if a document contains multiple instances of different forms of a given term, it will only be counted once. The percentage of documents containing a particular term over the total number of documents in each NSF directorate in a given year is reported.
Word Frequency Average Cosine Distance
This method collects all the words from all the abstracts in a given directorate and year and creates a list of each word and the frequency with which it occurs. The frequency distributions of words in individual abstracts are then compared with the overall frequency distribution of words in the directorate and year to which the abstract belongs. A mathematical technique called cosine distance is used to determine how different these two distributions are. A cosine distance of 0 means that the two distributions are exactly the same, whereas a cosine distance of 1 means that the distributions are maximally different. The cosine distances of the word distributions of every abstract from the distribution of its directorate and year are collected and averaged. This average cosine distance for a directorate and year provides a measurement of how similar or different from each other the abstracts in that group are based on word frequency.
Word Embedding Vectors Average Cosine Distance
Word frequency analysis only deals with language in a purely lexical sense. Using this tool, we can say certain things about the amount of variety in a corpus of text in terms of word frequency, but we cannot say anything definitive about the variety of ideas or meaning. It seems reasonable to expect that variety of words correlates with variety of meaning and ideas, but there may be certain cases where this does not hold true. In order to remedy this shortcoming of word frequency analysis, a second related method is applied.
Word embedding vectors are numerical encodings of words or symbols meant to capture their semantic and syntactic nature. Statistical machine learning techniques are used to generate these encodings by processing a tremendous amount of text. If words have similar meanings and are used similarly in natural language, then their word embedding vectors (numerical encodings) should be very similar. The word embedding vectors used in this work consist of 300 numerical values per token (word or symbol) to represent its semantic and syntactic properties. These encodings were generated using machine learning algorithms that analyzed approximately 600 billion words of text.
With these word embedding vectors, we can estimate the aggregate semantic and syntactic properties of a corpus of text by simply adding together the vectors of all the words in the corpus. We can then apply the same cosine distance technique to determine the difference (in terms of semantic and syntactic properties of words) between abstracts in a given directorate and year in the same way we have with the word frequency average cosine distance technique. This method puts forth a way of reinforcing the results of the word frequency method by providing an additional measure of document similarity.
Politicization of Awards
The following figures demonstrate a considerable rise in the frequency of award abstracts that contain selected politicized terms over the past 30 years. Additionally, the amount of funding granted to awards containing any of these terms is shown. To supplement these results, Appendix I gives general statistics on award abstracts, Appendix II offers a more detailed breakdown of the increase in incidence of individual terms in abstracts, and Appendix III provides data on the frequency of awards specifically containing two of the terms “diversity,” “equity” and “inclusion.”
As of 2020, across all fields 30.4% of successful grant abstracts contained at least one of the terms “equity,” “diversity,” “inclusion,” “gender,” “marginalize,” “underrepresented,” or “disparity.” This is up from 2.9% in 1990 (Figure 2). This increase is seen in every field. As of 2020, the two most politicized fields seem to be Education & Human Resources (53.8%, up from 4.3% in 1990) and Biological Sciences (43.8%, up from 6.6%), although “diversity” may sometimes have non-political connotations in the latter. Even the fields that should be most disconnected from politics have seen a massive jump in these terms: Mathematical & Physical Sciences went from 0.9% to 22.6%, and Engineering from 1.6% to 25.4%.
Note that word counts is a somewhat crude way of measuring politicization. Bias, particularly in the social sciences, is often subtle, and can apply to the kinds of questions that get asked and the standard of evidence used to accept or reject a hypothesis. Thus, the fact that so many grants contain terms that are in most contexts clearly associated with left-wing political causes likely underestimates the degree of politicization in science funding.
Measures of Document Similarity
The following figures demonstrate the decline in lexical and semantic distinctiveness of award abstracts over time. The techniques used to obtain these results are detailed more precisely in Appendix IV, along with the method for sampling words to correct for a correlation between average cosine distance and average abstract length.
When word frequency measures are used (Figure 6 and Figure 7), we see an increase in similarity between documents that is particularly pronounced beginning in 2017. When I use word embedding vectors (Figure 8 and Figure 9), which consider different words with similar meanings, the effect is present throughout the entire period of the dataset.
This report presents direct evidence that scientific funding at the federal level has become more politicized and less supportive of novel ideas since 1990. While the findings do not allow us to extrapolate back to periods of faster economic and scientific progress before the last few decades of the twentieth century, they certainly are suggestive and may be the latest iterations of longer-term trends. Future research should use other methods and datasets to measure the politicization of the scientific establishment and how supportive it has been of novel research ideas and approaches across time in order to provide more insights into the causes of scientific and technological stagnation.
Appendix I: General Statistics
This section graphically explores general properties of the dataset of NSF Awards from 1990 to 2020 analyzed in this work.
Appendix II: Individual Term Counts
This section presents the frequency of documents containing a number of terms associated with political activism.
Appendix III: DEI Awards
“Diversity,” “inclusion,” and “equity” are terms that tend to cluster together and are associated with a particular set of political programs. This section presents graphs showing the frequency of awards containing at least one of the forms of two of three of these terms.
Appendix IV: Technical Details
Parsing: Individual award data is obtained in archives by year from the NSF website.The data are available in XML format. The parser reads all of these and takes the award title, the award ID, the abstract narration, the start date, the award amount, the NSF directorate, and the primary investigator’s first name. The parser then repackages these data into large collections of award data aggregated by year and directorate.
Stop Words: In natural language processing removing “stop words” is a commonly used technique. Stop words are words that are very common in natural language but generally do not carry much meaning. All the words from a commonly used stop word list are removed from abstract titles and abstract narrations before any analysis is done.
Stemming: Stemming is another technique used in natural language processing that reduces words to their root form. This work uses NLTK (a publicly available natural language toolkit for the Python programming language) to stem words in the abstract titles and narration text. Stemming is used for word frequency distribution analysis but not for word embedding analysis.
Word Embeddings: Word embeddings are a way of representing individual words as vectors of real numbers for use in text analysis in natural language processing applications. Word embeddings are generally high dimensional vectors – the ones used in this work are 300 dimensions. If two words have a similar meaning, they will have vector encodings that are close together (measured in Cartesian distance.) Word embeddings can be obtained through several different machine learning techniques. A large amount of text will be used as input to a learning algorithm that will find good high-dimensional numerical encodings for each word such that words with similar meanings that are used similarly in writing will have similar encodings and words that have different meanings and usages will have encodings that are more distant from each other. This work uses a set of word embeddings that was trained on over 600 billion words of English text scraped from the web.
Word Frequency Cosine Difference Technique: To calculate the average cosine distance of word frequency distributions of individual abstracts to the overall word frequency distribution of all abstracts in the same year and directorate, the following method is used:
All stop words are removed from the title and abstract text of all awards. The remaining words are stemmed using the NLTK stemmer. The total number of instances of individual word stems in all awards in a given year and directorate are counted. Additionally, the total number of tokens (word stems) that occur in all the award texts in a given year and directorate are counted. The frequency of all individual stems is calculated by the ratio of occurrences of a given stem over the total number of tokens in all awards in that year and directorate. This can be thought of as a large vector of frequencies of words.
The word frequency distributions are similarly calculated for individual awards. The frequency of an individual word stem is the number of times that stem appears in the award text divided by the total number of words in the award text. If A is the word frequency distribution for an award and D is the word frequency distribution for the entire directorate the award belongs to, the cosine similarity is calculated by the following formula:
Because all the values of word frequencies are positive, Cosine Similarity can only have a value between 0 and 1, and Cosine Distance is merely 1 - Cosine Similarity. The Average Cosine Distance is then computed by taking the average of the computed Cosine Distance for all articles in a given year and directorate.
Aggregate Word Embedding Cosine Distance Technique: The set of word embeddings (Facebook Open Source, 2021) maps words to vectors of 300 real numbers. These vectors can be summed up to represent the aggregate semantic content of all the text in an entire award or all of the text in all awards in a given directorate in a given year.
The cosine distance of the aggregate word embedding vector of an individual award to the aggregate word embedding vector of all the text in all awards in the directorate and year to which it belongs can be calculated as follows:
The Average Cosine Distance of Aggregate Word Embeddings from the Mean is then computed by taking the average of the computed Cosine Distance for all articles in a given year and directorate.
There is a correlation coefficient of -0.49 between the average word length of award abstracts and the average cosine distance of word frequency distributions within each directorate and year. This effect occurs when all of the words in each individual award abstract are analyzed. To correct for this effect (which made it seem like cosine distance was decreasing more dramatically), instead of using all the words in the award text, 100 words were randomly sampled from each award and these words were used in the processes described in this Appendix to produce the document similarity findings shown in the Results section.
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