Bibliographic record
Abstract
Part I. Overviews of the Themes 1. There is No Aftermath for Women - Meredeth Turshen, Sheila Meintjes and Anu Pillay 2. Women in Conflicts, Their Gains and Their Losses - Codou Bop 3. Violence Against Women in The Aftermath - Anu Pillay 4. Problems of Identity, Solidarity and Reconciliation - Tina Sideris 5. War and Post-War Shifts in Gender Relations - Sheila Meintjes 6. Engendering Relations of States to Societies in the Aftermath - Meredeth Turshen Part II. Contemporary Experiences 7. Ambivalent Gains in Conflicts in South Asia - Rita Manchanda 8. Liberated, But Not Free: Women in Post-War Eritrea - Sondra Hale 9. Rape in War and Peace: Social Context, Gender, Power and Identity - Tina Sideris 10. Between Love, Anger and Madness: Building Peace in Haiti - Myriam Merlet 11. Caring at the Same Time: On Feminist Politics during the NATO Bombing of the Federal Republic of Yugoslavia and the Ethnic Cleansing of Albanians in Kosova, 1999 - Lepa Mladjenovic 12. Healing and Changing: The Changing Identity of Women in the Aftermath of the Ogoni Crisis in Nigeria - Okechukwu Ibeanu 13. Ambivalent Maternalisms: Cursing as Public Protest in Sri Lanka - Malathi de Alwis 14. 'We want Women to be given an Equal Chance': Post-independence Rural Politics in Northern Namibia - Heike Becker
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".