‘What happens in the room stays in the room’: conducting research with young men in the Canadian Hockey League
Bibliographic record
Abstract
In this paper, I examine the process by which I conducted interviews with 19 young elite-level ice hockey players who competed or were competing in the Canadian Hockey League (CHL). I explore some of the methodological issues that arose while conducting these interviews and engaging with the various gatekeepers that facilitated my access to these interviewees. I relate these issues to the broader field of qualitative methodologies and specifically work that examines the challenges when a woman researcher conducts interviews with men. Through this analysis, I investigate the ways that men’s elite-level hockey structures itself as a closed community allowing outsiders only restricted access to the institution and its players. I also discuss some of the contradictions in the research process, particularly as they pertain to access to players and what they will disclose during the interviews. By focusing on the interview process, I study the ways that a specific style of masculinity (and to a lesser extent, femininity) is produced through the interviews. I argue that one of the ways that a particular gender dynamic is produced within North American men’s and boys’ elite-level hockey is by allowing women (and others not associated with elite-level hockey) only limited and guarded participation.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.035 | 0.035 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".