What next for the studies of political science as a discipline? A tentative research agenda
Bibliographic record
Abstract
This text is an edited version of the opening remarks that Thibaud Boncourt, Past President of the Research Committee 33 (The Study of Political Science as a Discipline) of the International Political Science Association (IPSA) and associate professor at University Paris 1 Panthéon-Sorbonne / Centre Européen de Sociologie et de Science Politique (CESSP), gave at the special panel “The Future of the Studies of Political Science as a Discipline” sponsored by IPSA-RC33 at the 7th international interdisciplinary conference of political research SCOPE: Science of Politics (University of Bucharest, 20-24 September 2021, www.scienceofpolitics.eu). The event was organized and hosted by the Centre for the International Cooperation and Development Studies (IDC) of the Department of Comparative Governance and European Studies, Faculty of Political Science, University of Bucharest, and gathered participants from several countries on all continents, via a virtual meeting. The aim of the panel was to contribute to the global conversation on the current state of political science as a discipline, as well as to discuss the practical means through which IPSA-RC33 can contribute to it and to support the work of political scientists worldwide. Keywords: political science as a discipline, IPSA RC33, institutionalization, de-institutionalization, autonomization, gendering, postcolonizing
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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 | 0.066 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.012 | 0.050 |
| Scholarly communication | 0.034 | 0.076 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".