Publishing Trends in Political Science: How Publishing Houses, Geographical Positions, and International Collaboration Shapes Academic Knowledge Production
Bibliographic record
Abstract
Abstract Even though political science is one of the most extensive research fields within the social sciences, there is little scholarly knowledge about its publishing trends and the internationalization of the discipline. This paper analyzes international publishing by taking a close look at publishers, Scopus-indexed journals, articles, and author collaboration networks. The results show that the number of political science journals almost tripled between 2000 and 2022. Our descriptive analysis also reveals that only a few Western commercial international publishers, and Taylor & Francis in particular, dominate the publication of political science journals, and Western authors account for the majority of both academic papers and citations. Additionally, our research explores that the most prolific country in terms of publication within political science is still the United States, but the BRICS countries, especially India, Russia, and China, have achieved remarkable growth in their publication outputs. Finally, our network analysis suggests that the United States, the United Kingdom, Canada, and Australia occupy central positions in international collaborations among political scientists, but Asian, Eastern European and Latin-American regional networks have been developing in the last decade.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".