Political Science as a Dependent Variable: The National Science Foundation and the Shaping of a Discipline
Bibliographic record
Abstract
From 1965 to 2020, the National Science Foundation constituted the single largest funding source for political science research. As such, the NSF played a central role in defining the cutting-edge of our discipline. This study draws on historical records of the American Political Science Association to examine the political and administrative contexts that shaped the funding priorities of the NSF Political Science Program. Additionally, the study presents a new dataset and analysis of the nearly three thousand projects funded over the 55-year life of the program. The dataset shows that NSF funding was principally channeled toward quantitative research, whereas qualitative methods received little support, and work advancing normative, critical, or interpretive approaches received virtually no support. The archival record and awards-level data make visible the material forces that shaped knowledge production, and they underline the NSF’s instrumental role in consolidating behavioralism and marginalizing non-positivist approaches. The study sheds new light on the history of the discipline and helps to contextualize some of the distinctive features of American political science.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".