Writing the Body in Motion: A Critical Anthology on Canadian Sport Literature
Bibliographic record
Abstract
From the introduction: According to Don Morrow, who taught one of Canada’s first Sport Lit courses at the University of Western Ontario, Sport Literature is never just about sport. Rather, it explores the human condition using sport as the dominant metaphor. Similarly, Priscila Uppal, perhaps the most well-known Canadian scholar and writer to focus her attention on this topic, explains that the best sport literature does not focus exclusively on sport as sport; rather, in this literature, sport functions as “metaphor, paradigm, a way to experience some of the harsher realities of the world, a place to escape to, an arena from which endless lessons can be learned, passed on, learned again” (xiv). Many of the essays in this collection, therefore, examine the various ways sport functions metaphorically. Our authors also consider various recurring themes of sport literature, including: sport and the body; sport and violence; sport and gender; sport and society; sport and sexuality; sport and heroism; sport and the father/son relationship; sport and memory; sport and the environment; sport and redemption; sport and mortality; sport as religion; sport as quest; sport as place; and sport literature and intertextuality.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".