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| Criminal Career Paradigm× | Recidivism Survival Analysis× | |
|---|---|---|
| Lĩnh vực | Criminology | Criminology |
| Họ≠ | Process / pipeline | Survival analysis |
| Năm ra đời≠ | 1986 | 1988 |
| Người khởi xướng≠ | Alfred Blumstein, Jacqueline Cohen, Jeffrey Roth & Christy Visher | David R. Cox (method); Peter Schmidt & Ann Dryden Witte (criminological application) |
| Loại≠ | Conceptual framework for decomposing offending over the life course | Time-to-event regression for reoffending |
| Công trình gốc≠ | Blumstein, A., Cohen, J., Roth, J. A., & Visher, C. A. (Eds.). (1986). Criminal Careers and 'Career Criminals' (Vols. 1–2). National Academy Press. ISBN: 9780309036887 | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202. DOI ↗ |
| Tên gọi khác | Criminal Careers Framework, Career Criminal Paradigm, Offending Career Approach, Blumstein Criminal Career Model | Time-to-Recidivism Modeling, Recidivism Hazard Modeling, Failure-Time Analysis of Reoffending, Survival Analysis of Reoffending |
| Liên quan | 4 | 4 |
| Tóm tắt≠ | The criminal career paradigm is a framework for studying offending as a longitudinal sequence in an individual's life rather than as undifferentiated aggregate crime. Codified by Blumstein, Cohen, Roth, and Visher in the 1986 National Academy of Sciences report, it decomposes crime into distinct dimensions — whether someone offends (participation), how often active offenders offend (frequency, λ), and the onset, seriousness, and duration of the career — each potentially with different causes. | Recidivism survival analysis models the time from a release or index event until an individual reoffends, treating reoffending as a time-to-event ('failure') outcome with censoring for those not observed to fail. It applies survival methods — Kaplan-Meier curves, Cox proportional-hazards regression, and split-population models — to answer not just whether someone recidivates but how quickly and what raises or lowers that risk over time. |
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