Political Science Journals in Comparative Perspective: Evaluating Scholarly Journals in the United States, Canada, and the United Kingdom
Bibliographic record
Abstract
In this article we report the results from a new survey of political scientists regarding their evaluations of journals in the political science discipline. Unlike previous research that has focused on data from the United States, we conducted an Internet survey of political scientists in the United States, Canada, and the United Kingdom. We present data on journal evaluations, journal familiarity, and journal impact, both for our entire sample ( N = 1,695) and separately for respondents from each of the three countries. We document the overall hierarchy of scholarly journals among political scientists, though we find important similarities and differences in how political scientists from these three countries evaluate the scholarly journals in the discipline. Our results suggest that there is a strong basis for cross-national integration in scholarly journal communication, though methodological differences among the three countries may be an impediment.
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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 | 0.008 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.024 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".