1 Rights of the Body in Times of War
Bibliographic record
Abstract
this talk; Jennifer Deadman, who so ably facilitated my trip to Toronto; and all of the students and faculty in this exciting course on women’s health who have provided the occasion for my visit. I’m sure you share my concern about the hostile climate that people who are attempting to provide decent health services and to create inclusive and gender-equitable health policies are facing across the globe. I hope you share my conviction that we can do something to change this miserable landscape. Even here in Canada, with one of the most just and inclusive health systems on the planet, you are contending with increasingly high costs of pharmaceuticals and pressures in some provinces toward privatized insurance. And always, everywhere, divisions of gender, race and class intensify health inequities, even under the best systems. So, I want to clarify what I mean by the gendering of health access and health crises and to do so by framing that analysis in the overarching “global climate change” that has entrapped the world since Sept. 11, 2001: that of endless and limitless war. A widely read book by a long-time war correspondent from the US, Chris Hedges, has the troubling title, War Is a Force that Gives Us Meaning. Writing just after the events of Sept. 11, 2001, before its long-range political consequences were clearly evident, he is haunted by the shadow of Vietnam, El Salvador, Sarajevo, Bosnia, and Israel/Palestine. Hedges sees war
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".