Bibliographic record
Abstract
The scarcity of reviews of “gender and politics” books in disciplinary journals limits opportunities for “mainstream” political scientists to learn about the field. As chair of the jury to select the 2015 winner of APSA's Victoria Schuck prize for “the best book … on women and politics,” I realized that the field's size and diversity makes it hard to identify central themes, especially since feminist scholars are also active across the discipline's many fields and in the multidisciplinary enterprise of gender studies. In my CPSA presidential address (Vickers, 2015), I argued that although the “gender and politics” field has expanded greatly in its four-decade history, its impact on the discipline generally hasn't been transformative, since gender isn't being used as a key category of analysis in the discipline and the field's key theme that “the personal is political” isn't reflected in the discipline's main approaches, especially its dependence on a liberal conception of the private/public divide. This essay explores how the books reviewed use gender and this key theme in explaining contemporary political issues.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".