<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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.160 | 0.152 |
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".