Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Generalizations about women and peace are difficult especially for a white U.S. American who has not experienced war first-hand but whose government has conducted countless military operations around the globe. What I do hope to do here is to raise some questions that come from struggling from that location to be simultaneously a feminist human rights and anti-war/anti-imperialist activist. Acknowledging when and where we enter is a central tenet of feminist inquiry. Questions of women and peace/war are very particular having to do with the specificity of each conflict—-of time place race ethnicity class religion and other discrete circumstances—-as well as related to various social constructions of gender of masculinity and femininity. In that sense peace and the relation of women to war is a very local issue. And yet women and war/peace is also a very universal subject discussed in a variety of ways for centuries. Throughout the twentieth century and especially with the intensification of globalization and the rise of religious and ethnic fundamentalisms feminists have found it useful to make cross-cultural comparisons to share analysis and strategies as well as to build international solidarities for peace. (excerpt)
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it