Immigration, Crime and Criminal Justice Systems
Bibliographic record
Abstract
In a three volume collection Wolf Legal Publishers presents The Transnational Criminology Manual. We are happy with contributions from more than 100 eminent specialists from the field including scholars from, among others, France (Reims University, Department of Justice) Canada (Montreal University), The Netherlands (Tilburg University, Leiden University, Erasmus Medical Centre), USA (New York University, Duke University), Belgium (Free University Brussels) and the UK (University of Exeter, Strathclyde University, Cardiff University), this Encyclopedia provides an elaborate insight in criminology and its specifics. The Transnational criminology manual provides comprehensive coverage of the leading topics in criminology. Whereas the first volume provides the readers with an introduction to criminology, the second and third volume include timely topics such as internet crimes, money laundering, victimization and therapy. This collection is bilingual; English / French, whereas most contributions are presented entirely in English (with French abstracts). The books are of interest for students in criminology, psychology, sociology and law, social workers, judges, attorneys, probation officers, policemen, and many others.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".