Submitting to<i>Politics & Gender</i>: Advice from the Editors
Bibliographic record
Abstract
For nearly 20 years,Politics & Genderhas been a leading outlet for research on women, gender, and politics. As past and current editors,1we are happy to share our advice for early career researchers interested in submitting manuscripts to the journal. We believe that as the official journal of the Women, Gender, and Politics Section of the American Political Science Association, the content ofPolitics & Gendershould reflect the diversity of authors, methods, and topics found across the broader gender and politics research community. However, not all authors have the full information on how to best prepare their manuscripts—or, indeed, what to expect during different parts of the review process (see Anlar and Phillips 2023).
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 | 0.040 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.042 | 0.062 |
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".