Bibliographic record
Abstract
Sport is one of the key recommendations in the TRC's final reoort, and it is imperative that scholars of sport literature and culture take this seriously. Hockey, as Canada's national sport, is a critical place to begin. It is assumed that hockey is unifying, but it is a “contact zone” (Pratt) where “players” present competing narratives about the meaning of hockey, “our game,” in a post-TRC (Truth and Reconciliation Commission) Canada. Here I present a contact zone reading of two books about hockey: Stephen Harper’s A Great Game (2013) and Richard Wagamese’s Indian Horse (2012). The books were published a year apart and each one has national significance: Harper’s history was published when he was the sitting Prime Minister, and Wagamese’s novel was a strong contender in CBC’s “Canada Reads” in 2013. Harper presents a neat progress narrative (from amateur to professional hockey), while Wagamese refuses the conventional narrative of hockey development and progress, and tracks the movement away from professional to community-based hockey. In Indian Horse both hockey and masculinities undergo a process of truth and reconciliation, and hockey is provided a far more nuanced narrative than Harper’s text allows. Key Words: Postcolonial Hockey; Canadian Masculinities; Wagamese; Harper
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.074 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".