Understanding the Outlaw Motorcycle Gangs: International Perspectives
Bibliographic record
Abstract
The Outlaw Motorcycle Gangs (OMCGs) are, without doubt, one of the most interesting, diverse, eclectic, social groups in society today. They are considered outsiders, deviants, and more often than not they are seen as criminally organized. Yet we know so little about them, their structure, organization, and their ethos. Still, society legislates, monitors, and controls, in an effort to police the behaviors we know so little about. In this new and extremely informative text, Bain and Lauchs bring together a number of subject experts from around the world in an effort to explain the development, growth, and global expansion of the Outlaw Motorcycle Gangs. For the first time, this text brings together discussions of the OMCGs from Canada, the United States, South and Central America, Europe, Australia and New Zealand. This text should be compulsory reading for anyone interested in the examination or investigation of this group in society today. It is particularly valuable to criminal justice students, those studying social groups, gangs and organizations, or the sociology of deviance. However, it is also just as relevant for professionals working within the criminal justice and/or legislative field.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".