Hollywood Incarcerated and on Death Row: Bjork, Schwarzenegger, and the Pedagogy of Retribution
Bibliographic record
Abstract
Today, the United States accounts for less than 5 percent of the world population, but almost a quarter of the world’s prisoners, a total of over 2.3 million citizens (750 per 100,000 and 1 in 100 among adults). The rise in the prison population accelerated dramatically in the 1970s with “tough on crime” laws and the inception of the drug wars (Liptak, 2008). Among the many troubling aspects of this prison-industrial complex is the racial composition of prisons today. Blacks are 6.4 times as likely as whites to be incarcerated and, among 25- to 29-year-olds, 12.6% are in prison, versus 3.6% of Latinos and 1.6% of whites. An estimated 32% of Black males will enter state or federal prison at some point in their lives (versus 17% of Latinos and 5.9% of white males), and over half the total prison population is in jail for drug-related crimes. Overall, an incredible 7 million people were under some form of correctional supervision in 2005, with the vast majority minorities (Justice, 2008). And the United States remains the only liberal democracy in the world that still practices capital punishment. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".