Juvenile delinquency and justice : sociological perspectives
Bibliographic record
Abstract
Preface - the Editors. Juvenile Justice and Delinquency in Historical Perspective. Introduction - the Editors. The Child-Saving Movement and the Origins of the Juvenile Justice System - A. Platt. Best-Laid Plans: The Ideal Juvenile Court - E. Ryerson. History Overtakes the Juvenile Justice System - T. N. Ferdinand. The Measurement and Social Distribution of Delinquency. Introduction - the Editors. Gangs, Drugs, and Delinquency in a Survey of Urban Youth - F. -A. Esbensen and D. Huizinga. The Impact of Sex Composition on Gangs and Gang Member Delinquency - D. Peterson, J. Miller, and F. -A. Esbensen. The Social Psychology of Delinquency. Introduction - the Editors. Scared Straight: A Question of Deterrence - R. J. Lundman. Social Learning Theory, Drug Use, and American Indian Youths: A Cross-Cultural Test - L. T. Winfree, Jr., C. T. Griffiths, and C. S. Sellers. Delinquents' Perspectives on the Role of the Victim - C. Carpenter et al. Self-Definition by Rejection: The Case of Gang Girls - A. Campbell. Explaining School Shooters: The View from General Strain and Gender Theory - R. J. Berger. Social Structure and Delinquency: Family, Schools Community, and Work. Introduction - the Editors. Family Relationships and Delinquency - S. A. Cernkovich and P. C. Giordano. Players and Ho's - T. Williams and W. Kornblum. Getting Rid of Troublemakers: High School Disciplinary Procedures and the Production of Dropouts - C. Bowditch.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".