Bibliographic record
Abstract
Abstract This chapter offers an autotheoretical account of my experiences as a trans man training for my first amateur bout – one that has yet to come but borne out of a never-ending fight. My chapter is in conversation with autobiography (McBee, 2018), journalistic (Oates, 2006) and ethnographical scholarship addressing the intricacies of pugilistic violence as a response to systemic gender, racial, sexual and economic oppression (Beauchez, 2017; Rutter, 2007). Boxing draws fighters from marginalized communities. As a trans man, I have fought intense ‘negative’ feelings most of my life – emotions culminating into rage. I joined an amateur boxing club in Ottawa after trying to instigate a street altercation with a stranger. Feeling out of control, I sought refuge with others who also believe fighting solves problems. Influenced by Oates' observations that boxing is ‘primarily about being, and not giving, hurt’ (2006) and sharing McBee's experience of ‘loving those men even as I hit them in the face, and knowing that they love[] me back’ (2018), I explore boxing as intimate and affective grounds for bearing witness to the pain and injury of the other shaping their daily lives. Amateur boxing as an embodied and affective space exceeds the oft reductionist (mis)understanding of the sport as a violent spectacle of individual bravado and the emphasis scholars and the mainstream media place on the ‘heroic body’ (Woodward, 2007); instead, I offer glimpses into the healing justice as social justice that witnessing the pain, vulnerability and resilience of oneself and other boxers can provide.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".