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Record W2528587759

Understanding the Outlaw Motorcycle Gangs: International Perspectives

2017· book· en· W2528587759 on OpenAlexaboutno aff
Andrew Bain, Mark Lauchs

Bibliographic record

VenueQUT ePrints (Queensland University of Technology) · 2017
Typebook
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsEthosDeviance (statistics)CriminologyOrganised crimeCriminal justicePolitical scienceLegislatureSubject (documents)SociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.807
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.079
GPT teacher head0.304
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations32
Published2017
Admission routes1
Has abstractyes

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