Bibliographic record
Abstract
Terror, Counter-Terror: Women Speak Out presents articles by severalwomen writers and women’s organizations. The book analyzes and interrogatesthe madness of male-dominated war and violence, and presentswomen’s perspectives on war and the 9/11 tragedy. Contributors includefeminist writers, authors, academics, and journalists; mothers, women ofcolor, Muslim women; and women who have had first-hand experiencewith war and its effects. The editors provide an excellent critical reappraisal of the ideas, concepts,and language that underpin the multilayered world of war, power, andpeace. The book also explores diverse women’s perspectives on the failure ofwar to bring about peace. In giving their perspective, the authors respond eloquentlyand defiantly to war’s destructive nature. This collection, a wonderfulanthology of women’s experiences of war, allows the reader to capture thesuffering of war as well as its paradoxes, double standards, and contradictions.The essays are organized into seven sections: “Personal and Political,”“The War on Terror,” “Saying No,” “Motherland/ Fatherland,” “The War onWomen,” “Displaced and Dispossessed,” and “Women against War.”The book highlights the wars in Afghanistan and Israel and the 9/11tragedy. The authors lament that war has never really brought peace, butrather turmoil and human and economic suffering. Most people in theWest see sanitized images of war that are carefully selected for them.Women Speak Out tells the story of how loosing one’s children, home, andlivelihood are part of war’s true horrors ...
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".