1 Pathways and Crime Prevention: A Difficult Marriage?
Bibliographic record
Abstract
For at least a quarter of a century the study of the developmental origins of crime and delinquency has been, in the words of Nagin and Tremblay (2005, p. 873), “both an important and contentious topic in criminology. ” Indeed, as these researchers note, the contemporary international wave of research within this genre is “testimony to the central position of what has come to be called developmental criminology ” (p. 874). We estimate that since 1990 the journal Criminology has devoted at least one third of its articles to some aspect of developmental criminology. Many other journals, broader perhaps in their theoretical and methodological orientations than Criminology, are also now devoting considerable space to themes such as the effects of child abuse and family violence on children and young people’s personal development, or the influence of poverty and social exclusion on pathways towards adulthood and perhaps toward crime. Our aim with this book is to add to this growing body of knowledge, particularly from an Australian and United Kingdom perspective, and to highlight some important theoretical, methodological and policy debates. Drafts of all the chapters were
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".