Bibliographic record
Abstract
Abstract Imperial Sexism: Why Culture and Women’s Rights Don’t Clash debunks the presumption that culture and women’s rights are inherently at odds. Rather than a clash of civilizations, the problem is imperial sexism, or racism and sexism rooted in the colonial era and their compounding harms. By comparing three policy debates—the French ban on the full-face veil adjudicated by the European Court of Human Rights, the legalization of polygyny in South Africa, and the repeal of the “marrying-out” rule in Canada—the book reveals why a clash is never inescapable, why the presumption of a clash endures, and the damage that ensues. The key to these insights lies with minoritized women who harmonize cultural, religious, and women’s rights, tackling imperial sexism while emphasizing shared values across cultures. By amplifying these women’s voices, the book dispels the illusion of an unavoidable clash, discusses how to overturn it, and offers a promising alternative. Imperial Sexism explains how to advance justice for minoritized women and their communities while strengthening the indivisibility of human rights and mutual coexistence.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".