Sexual violence: policies, practices, and challenges in the United States and Canada
Bibliographic record
Abstract
Sexual Violence: Policies, Practices, and Challenges by James F. Hodgson and Debra S. Kelley The Measurement of Rape by Debra S. Kelley Biology, Sex, and the Debate over Chemical Castration by Craig T. Palmer, Randy Thornhill, and David N. DiBari A Critical Critique of the Cultures of Control: A Case Study of Cyber Rape by Livy A. Visano Sexual Assault Behind Bars: The Forgotten Victims by Charles Crawford Rape Law Reform by Frances P. Bernat Myths of Women and the Rights of Man: The Politics of Credibility in Canadian Rape Law by Margaret Denike Psychological Evidence in Sexual Assault Court Cases: The Use of Expert Testimony and Third Party Records by Trial Court Judges Giannetta Del Bove and Lana Stermac Re-Conceptualizing Sexual Assault from an Intractable Social Problem to a Manageable Process of Social Change by K. Edward Renner Law Enforcement's Response to Sexual Assault: A Comparative Study of Nine Counties in North Carolina by Vivian B. Lord and Gary Rassel Policing Sexual Violence: A Case Study of Jane Doe versus the Metropolitan Police by James F. Hodgson Assessing the Role of Sexual Assault Programs in their Communities by Elizabethann O'Sullivan Megan's Law in California: The CD-ROM and the Changing Nature of Crime Control by Suzette Cote Revisiting Megan's Law and Sex Offender Registration: Prevention or Problem by Robert E. Freeman-Longo
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".