Should Political Science Be More Relevant? An Empirical and Critical Analysis of the Discipline
Bibliographic record
Abstract
This paper arises from the empirical evidence about trends, issues and perspectives in political science to be found in the International Political Science Association’s (IPSA) Research Committee 33 book series entitled: The World of Political Science: Development of the Discipline and the papers presented at the 2008 Montreal Conference of the IPSA on New Theoretical and Regional Perspectives on International Political Science. One of the issues raised by this analysis of the discipline’s strengths and weaknesses is the question of whether political science is relevant to the outside world and if not, why not? It is evident to the naked eye that in comparison with, say, economists (President Obama has three advisory councils), political science is of relatively little interest to policy-makers, the media and the public. We have to ask if political science is out of step with the world and, if so, what might be done about it?
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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 | 0.077 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.016 | 0.126 |
| Scholarly communication | 0.031 | 0.047 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 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".