Bibliographic record
Abstract
This global survey starts from the assumption that the significant transformations in women's lives deserve to be fully documented and interpreted. Janet Mancini Billson and Carlyn Fluehr-Lobban tackle the complexities of social change by using data from countries in every world region to illustrate the most critical challenges that women faced during the last century - challenges that are also likely to shape the 21st century. Global knowledge and feminism dovetailed in the 20th century, fed by international air travel, telecommunications, the internet, and a growing awareness that solving female oppression would improve the lot of all humankind. The authors therefore adopt a strong international, comparative, cross-cultural, and feminist framework that uncovers the fundamental processes that promote, sustain, or degrade the female condition. At the heart of Female Well-Being are case studies written by country teams of scholars, educators, and policy analysts, in Canada, The United States, Colombia, Iceland, the
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".