Introduction: Transnational and Comparative Criminology in a Global Perspective
Bibliographic record
Abstract
Any criminology worthy of the name should contain a comparative dimension. The contents of cultural meanings that are loaded into the subject of criminology are too variable for it to be otherwise. It is fair to say that most of the important points made by leading scholars of criminology are comparative in nature. It is just that the basis of comparison is often relatively narrow. For example, the hyphen that both separates and binds the phrase ‘Anglo-American criminology’ implies an obvious basis for comparison, although it remains within ‘Anglophonia’. British criminology invites comparison between England, Wales, Northern Ireland and Scotland, even if the differences are too often disregarded by criminologists there. Federal systems such as Australia, Canada and the United States offer a good basis for comparative work and European criminology provides ample room for comparison, with added richness due to wide linguistic and national variability. At a real stretch comparative criminology would go for total global reach and try to touch upon matters of criminological concern on all the major populated continents of the world. Such an undertaking is rare indeed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".