Boxing Ain’t No Game: Clement Virgo’s <i>Poor Boy’s Game</i> as Canadian Racial Counter-narrative
Bibliographic record
Abstract
In its exploration of racialized poverty, carcereal regimes and the disciplining of Black bodies in social space, Clement Virgo’s Poor Boy’s Game provides a radical counter-narrative to Canada’s dominant narrative of benevolence and racial tolerance. Few critics, however, were willing to consider the film in these terms. Reviewers were largely unable—or unwilling—to discuss the film’s visual gestures to slavery, an absence that I argue speaks to the nation’s inability to acknowledge this aspect of its history or to speak meaningfully about questions of race. Instead, critics relegated the film to the status of a boxing movie, and their repeated and disproportionate emphasis on sport was one means through which they attempted to minimize the social and political issues the film attempts to address. While sport is often assumed to be apolitical and has historically been seen as the opiate of the masses, critics like C. L. R. James, in Beyond A Boundary (1963), have observed that societal prejudices are often rehearsed in the microcosm of the playing field, or in Virgo’s representation, the boxing ring. This paper situates Poor Boy’s Game within this framework, to emphasize the ways the film also writes a counter-narrative of athleticism which challenges the historical role of sport in the writing of dominant national narratives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.011 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".