Bibliographic record
Abstract
Discourses of Denial: Mediations of Race, Gender, and Violence , Yasmin Jiwani, Vancouver: UBC Press, 2006, pp. viii, 255. At first glance many political scientists may not see Discourse of Denial as an intervention that speaks to their discipline. After all, Jiwani's examination of racism, sexism and violence in Canada is explicitly directed to those who traverse multiple and interdisciplinary boundaries, racialized young women and immigrant women, front-line feminist anti-violence, anti-racist and anti-poverty activists, as well as policy makers. However, political scientists can gain much from this persuasively argued, methodologically diverse, innovative and well-researched book. Jiwani addresses how certain institutions—in particular, the dominant media—serve to “mediate” violence. Mediations involve discursive strategies that give recognition to certain expressions of violence and completely erase others, especially racism. Since political scientists frequently rely on the media in their research and teaching, and serve as media commentators as part of their community service, there is much here that is thought-provoking.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.035 | 0.077 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".