A securitização dos Clubes de Motociclistas: estudo de caso do Hells Angels Motorcycle Club no Canadá
Bibliographic record
Abstract
This dissertation proposes an in-depth investigation into the historical evolution and contemporary perception of motorcycle clubs, with a particular emphasis on outlaw clubs, from their origins to the present day. The research adopts a qualitative approach, combining a thorough review of existing literature with a detailed analysis of data to provide a comprehensive understanding of the role of these clubs in society. Throughout the text, various aspects are explored, including the formation and consolidation of motorcycle clubs, their relationship with transnational organized crime, and the narrative construction portraying them as a threat to public security. A specific focus is given to the case study of the Hells Angels Motorcycle Club (HAMC) in Canada, recognized as one of the most emblematic organizations in the country. Furthermore, through critical examination, the research addresses gaps in the field, particularly regarding the characterization of clubs as criminals organizations and the varying levels of their involvement in illegal activities. Finally, the strategies adopted by authorities to deal with these groups are discussed, highlighting the application of securitization theory as a lens for understanding policies related to outlaw motorcycle clubs. In conclusion, the dissertation aims to contribute to future research on One Percent clubs and their classification as criminal organizations, emphasizing the need for further studies on the formation of clubs, their transnational dynamics, and the impact of securitized countermeasures on these organizations
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".