Profile of Contributors to the American Political Science Review, 2010
Bibliographic record
Abstract
This study examines the profile of contributors of full-length articles to the American Political Science Review (APSR) in 2010. Of the 79 different contributors, almost 9 (86.1%) out of every 10 are men. Whites accounted for over 9 (93.7%) out of every 10 contributors. Full professors accounted for 35%, the highest rate, with assistant professors accounting for 31 percent. Yale University, Harvard University, University of Illinois-Champaign, Florida State University, Massachusetts Institute of Technology, University of California-San Diego, and the University of Chicago, all employ 3 or more of these contributors. Almost 94% of the contributors have a Ph.D. Almost 89% of the contributors earned their terminal or highest degrees in political science/government. Harvard University, the University of Chicago, the University of Rochester, the University of California-Berkeley, and Duke University, all conferred 4 or more terminal or highest degrees to these contributors. The study presents explanations for these results, focusing on the underrepresentation of women and minorities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".