Effective Aggressiveness and Inconsistencies in the Bijuridical Treatment of Aggressive Behaviour: Mixed Martial Arts, Bullying, and Sociolegal Quandaries
Bibliographic record
Abstract
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.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.069 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".