MétaCan
Menu
Back to cohort
Record W4367159354 · doi:10.7202/1078136ar

Intra-Group Diversity and How it is Managed by an Outlaw Motorcycle Club

2021· article· en· W4367159354 on OpenAlexaff
Daniel R. Wolf

Bibliographic record

VenueCulture · 2021
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsClubDiversity (politics)Subculture (biology)RepertoireTheme (computing)SWORDAsset (computer security)CamouflagePsychologyPolitical scienceEngineeringComputer securityLawComputer scienceArtWorld Wide WebEcologyLiteratureBiology

Abstract

fetched live from OpenAlex

A deviant subculture such as an outlaw motorcycle club must be able to accommodate internal diversity because a repertoire of differing talents, ideas, and attitudes facilitates an adequate response to the disparate demands placed upon the organization. At the same time that intra-member variation is an asset, however, diversity frequently must be camouflaged in order to maintain the integration necessary to survive in a hostile environment. The need for camouflage varies in relation to the degree of perceived threat. This theme is examined in three different contexts: the clubhouse, the club bar, and violent encounters with outside society.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.349

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCultureSame topicHate Speech and Cyberbullying DetectionFrench-language works237,207