We Know a Lot, but Not Nearly Enough: Introduction to the CJCCJ Special Issue on Desistance
Bibliographic record
Abstract
Desistance is now one of the main criminal career parameters investigated by criminologists. Similarly, practitioners working within the criminal justice system are primarily focused on ways to promote desistance among their clients. However, these two groups typically think about desistance in different ways. Practitioners are often exposed to the idea from correctional psychology that desistance is the absence of recidivism. Criminologists typically consider desistance to be a process that includes recidivism. The purpose of this special issue was to present a criminological viewpoint of desistance. Authors of each article identified an area that they felt was a key or emerging theme in desistance research. This article introduces the topic of desistance, highlights how the articles in this special issue contributed to desistance research and have implications for criminal justice system practices, and ends with a call for future research on the measurement of human agency, structural and historical contexts that influence human agency, and whether human agency moderates the relationship between informal social control and desistance.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".