Knocking Down Walls in Political Science: In Defense of an Expansionist Feminist Agenda
Bibliographic record
Abstract
Abstract This article offers one possible answer to the question “What is the future of feminist political science?” by outlining and defending an expansionist agenda that is centred on challenging the male-female binary that has been upheld and replicated in the discipline to date. Such an approach draws heavily on the insights of intersectional analyses, transgender, queer and gender-fluid articulations of identity and requires that the field of political science investigate the varied and complex gendered experiences of “men.” Overall, this article argues that such as expansionist agenda is key to responding to the interrelated challenges presented by the perceived “crisis” of feminism and the ongoing “masculinity” of the discipline of political science.
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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 | 0.076 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.171 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.016 | 0.023 |
| 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".