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Record W3124996680

Effective Aggressiveness and Inconsistencies in the Bijuridical Treatment of Aggressive Behaviour: Mixed Martial Arts, Bullying, and Sociolegal Quandaries

2014· article· en· W3124996680 on OpenAlexaffabout
Sara Ross

Bibliographic record

VenueeYLS (Yale Law School) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsYork University
Fundersnot available
KeywordsLegislationLegislatureContext (archaeology)Martial artsEthnographyPsychologyPolitical scienceSocial psychologyCriminologyCounterexamplePublic relationsSociologyLawGeographyArtVisual artsMathematics
DOInot available

Abstract

fetched live from OpenAlex

This paper seeks to address effective aggressiveness and the treatment of aggressive behaviour in the context of MMA in comparison to the balance of the formal Canadian legal landscape. I choose anti-bullying legislation, and its treatment of aggressive behaviour, as a counterexample to the treatment of aggressive behaviour within the MMA regulatory framework. By intertextually linking and superimposing these two categories of legislation, a critical lens drawing on institutional ethnography is applied. This is done to question and deconstruct the differential treatment of aggressive behaviour and the rationale behind the legislative mixed message sent. This lens also allows me to show the importance of a more thorough analysis and understanding of the imported internal frameworks of regulated activities that are candidates for decriminalization through amendments to Canada’s Criminal Code intended to ensure the Criminal Code is current to today’s reality. The quandary faced within the fabric of the MMA community regarding its own treatment of aggressive behaviour, where it is both reified as well as castigated through anti-bullying advocacy, will also be examined.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.017
GPT teacher head0.292
Teacher spread0.275 · 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 designObservational
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
Published2014
Admission routes2
Has abstractyes

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