Race, ethnicity, sexual orientation, violent crime : the realities and the myths
Bibliographic record
Abstract
Contents * Editor's Foreword: From the Shadows into the Light: The Burden of Inconvenient Knowledge? * Blacks and Whites as Victims and Offenders in Aggressive Crime in the U.S.: Myths and Realities * Race as a Variable in Imposing and Carrying Out the Death Penalty in the U.S. * Patterns of Violent Behavior and Victimization Among African American Youth * Comparing the Behaviors and Social Environments of Offending and Non-Offending African-American Adolescents * Identity Diffusion and Development Among African Americans: Implications for Crime and Corrections * Level of Moral Reasoning Among African-American and Caucasian Domestic Violence Offenders Prior to Targeted Professional Intervention * Violent Victimization and Fear of Crime Among Canadian Aboriginals * Adapting Violence Rehabilitation Programs for the Australian Aboriginal Offender * Violence Against Gays and Lesbians * Index of Topics and Names * Reference Notes Included
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".