Book Note: Talking About Torture: How Political Discourse Shapes The Debate, by Jared Del Rosso
Bibliographic record
Abstract
FOLLOWING THE SEPTEMBER 11 ATTACKS and the subsequent “War on Terror,” allegations of torture (or “enhanced interrogation”) sparked intense debates about the rights of the detainees, political accountability, and legality of actions. Today, it is taken for granted that the United States participated in the abuse of detainees at facilities. In Talking About Torture: How Political Discourse Shapes the Debate by Jared Del Rosso1, society’s acknowledgment of torture is not taken for granted. Instead, Del Rosso provides the reader with an analysis on how the discourse on torture in the US transitioned from denial of its existence to acknowledgment. The introductory chapter outlines the purpose of the book, the reason behind focusing on the discourse on torture rather than a direct study of the use of torture, methodology, and goals. In the first chapter, the author sets the foundation by proposing that “torture” is a cultural object associated with certain imagery and meaning. In the past, torture was once a “neutral word within the legal vocabulary”—today, it is a word “packed with moral meaning and humanitarian principles of human rights and inherent dignity.”2
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".