Bibliographic record
Abstract
abuses committed by US military on Iraqi prisoners at the CBS television show 60 Minutes II. The scandal was instantaneous and tremendous. This event has been the starting point of my Master’s thesis and I propose to examine here some of my findings. The U.S. administration rapidly explained that the acts had been committed by a few “bad apples”, i.e. sadistic members of the military who had tarnished the image of the Unit-ed States around the world. However, international events contemporaneous to the scandal provided plenty of examples of situations similar to Abu Ghraib. Among other things, Hu-man Rights Watch released a report in July 2006 in which it was explained that “[t]orture and other abuses against detainees in U.S. custody in Iraq were authorized and routine, even after the 2004 Abu Ghraib scandal ” (HRW 2006). How did this situation come about? Based on the Master’s research I conducted, I will argue here that some U.S. governmental discourses created what has been called a “torture-sustaining reality ” that produces a favor-able environment to commit torture acts. In order to do that, I will first seek to explain how the government discourses are connected to the actions perpetrated in Abu Ghraib. Second, AriAne BélAnger-Vincent is a PhD student at Laval University, Quebec.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".