Life-Course Criminology: Contemporary and Classic Readings
Bibliographic record
Abstract
Section I: The Life-Course Perspective. 1. Time, Human Agency, and Social Change: Glenn H. Elder, Jr. 2. Crime and Deviance in the Life Course: Robert Sampson and John H. Laub. Section II: Age, Crime, And Criminal Careers. 3. Introduction: Studying Criminal Careers: Alfred Blumstein, Jacqueline Cohen, Jeffrey Roth, and Christy Visher. 4. The True Value of Lambda Appears to Be Zero: An Essay on Career Criminals, Criminal Careers, Selective Incapacitation, Cohort Studies, and Related Topics. Michael Gottfredson and Travis Hirschi. Section III: Life Course Theories Of Criminal Behavior. 5. Adolescence-Limited and Life-Course Persistent Antisocial Behavior: A Developmental Taxonomy: Terrie Moffitt. 6. A Life-Course Theory of Cumulative Disadvantage and the Stability of Delinquency: Robert Sampson and John H. Laub. Section IV: Empirical Tests Of Life-Course Theories. 7. Life Course Trajectories of Different Types of Offenders: Daniel S. Nagin, David P. Farrington, and Terrie E. Moffitt. 8. Criminal Careers in the Short-Term: Intra-Individual Variability in Crime and Its Relation to Local Life Circumstances. Julie Horney, D.Wayne Osgood, and Ineke Haen Marshall. Section V: Developmental Versus Static Theories: Current Debates. 9. Control Theory and the Life-Course Perspective: Travis Hirschi and Michael R. Gottfredson. 10. Understanding Variability in Lives Through Time: Contributions of Life-Course Criminology. Robert J. Sampson and John H. Laub. Section VI: Understanding Persistence In Criminal Behavior. 11. Stability of Aggression Over Time and Generations: L.R. Huesmann, L.D. Eron, M.M. Lefkowitz, and L.O. Walder. 12. Generality, Continuity, and Change in Offending: Raymond Paternoster, Charles W. Dean, Alex Piquero, Paul Mazerolle, and Robert Brame. Section VII: Examining Desistance From Criminal Behavior. 13. Age, Differential Expectations, and Crime Desistance: Neal Shover and Carol Y. Thompson. 14. Trajectories of Change in Criminal Offending: Good Marriages and the Desistance Process: John H. Laub, Daniel S. Nagin, and Robert J. Sampson. Section VIII: Interventions To Reduce Crime Over The Life Course. 15. Parent and Child Training to Prevent Early Onset of Delinquency: The Montreal Longitudinal-Experimental Study: Richard Tremblay, F. Vitaro, L. Bertrand, M. LeBlanc, H. Beauchesne, H. Boileau, and H. David. 16. Long-Term Effects of Nurse Home Visitation on Children's Criminal and Antisocial Behavior: 15-year Follow-up of a Randomized Controlled Trial. David Olds, Charles R. Henderson, et al.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".