Sport at the Conjuncture: Sport History, Sexual Abuse, and Resistance
Bibliographic record
Abstract
Abstract Stuart Hall stated “the university is a critical institution or it is nothing.” When it comes to the historical study of sexual abuse in Canadian sport, until very recently, it has been very much the latter. Nothing. As part of a larger project on studies of sexual abuse in sport, we reviewed articles across the four leading sport history journals – Sport History Review, Sport in History, Journal of Sport History, and International Journal of the History of Sport – to consider what methods, sports, and demographics received the most analysis. Such an effort proved impossible. There was scholarly silence on the matter. But this raised another question. So what? Would publishing in pay-walled academic journals about so pressing a societal issue make any difference at all? Furthermore, can a PhD-touting academic – including the lead author of this paper – ever enact change via the field of history if their sole purpose is to churn out studies for the ivory tower? We think not. It requires boots on the ground. Engagement and collaboration with those Antonio Gramsci called “organic intellectuals,” so we can tend the flames of knowledge and fuel a movement. History can be the tool one wields. Public, digital history.
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 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".