Bibliographic record
Abstract
1. Sex and Gender in Paleoanthropology Lori D. Hager, University of California, Berkeley, 2. Good Science, Bad Science, or Science as Usual?: Feminist Critiques of Science Alison Wylie, University of Western Ontario, 3. Is Primatology a Feminist Science Linda Marie Fedigan, University of Alberta, 4. Mothers, Labels, and Misogyny Rebecca Cann, University of Hawaii at Manoa, 5. The Paleolithic Glass Ceiling: Women in Human Evolution Adrienne Zihlman, University of California, Santa Cruz, 6. Brain Evolution in Females: An Answer to Mr. Lovejoy Dean Falk, State University of New York at Albany, 7. Has Estrus Been Lost in Hominids? Becky A. Sigmon, University of Toronto, 8. A Pound of Biology and a Pinch of Culture or a Pinch of Biology and a Pound of Culture?: The Necessity of Integrating Biology and Culture in Reproductive Studies Susan Sperling, University of California, San Francisco and Yewoubdar Beyene, University of California, San Francisco, 9. Female Proto-Symbolic Strategies Camilla Power, University College London and Leslie Aiello, University College London, 10. Mobilizing Ideologies: Paleolithic Art, Gender Trouble, and Thinking About Alternatives Margaret W. Conkey, University of California, Berkeley
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".