<i>PS: Political Science and Politics</i> Reviewers, 2006
Bibliographic record
Abstract
In 2006, PS: Political Science and Politics published myriad articles covering a vast range of topics. Symposia alone published in 2006 focused on inequality in American democracy, on the rejection of the EU Constitution, on how the law affects the actions of department chairs, on the methodology of field research in the Middle East, on voting gaps in the 2004 election, on political corruption, and on the politics of Canada. Upcoming symposia will focus on the U.S. military, on how to incorporate the politics of the Iberian Peninsula into your syllabus, on the future of state election reform, on Islamic extremism in Europe, and on the importance of congressional leadership selection. And remember, these are only the symposia. The journal's commitment to publishing articles on pedagogy and on the profession, as well as exemplary topical scholarship on a wide array of topics, calls for an equally broad stable of expert reviewers. PS cannot publish such diverse work without the outstanding work (and open-mindedness) of our peer reviewers. The peer-review process relies on the professionalism and generosity of those who contribute their time to read and evaluate the work of others. The editors of PS thank the following scholars, who served as manuscript reviewers between January 1, 2006, and December 31, 2006. Very special thanks go to those scholars whose names appear in bold; they reviewed for PS in both 2005 and 2006.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".