Discrimination in Canadian Minor Hockey
Bibliographic record
Abstract
Pour un joueur canadien de hockey mineur, il existe un rapport étroit entre son mois de naissance et les chances qu’il aura de jouer dans une équipe de très haut niveau. Les joueurs nés dans les premiers mois de l’année sont avantagés. On attribue généralement ce fait au système de classification, dans le hockey mi-neur, qui groupe les joueurs en catégories selon l’âge. Dans cet article, nous commençons par un examen de la situation actuelle. Ensuite, nous montrons que le système de classification basé sur l’âge ne constitue pas une explication suffisante; il faut aussi compter avec une répartition qui s’opère très tôt (c-à-d. la division des joueurs en équipes représentatives et en équipes de ligue-maison.) Nous suggérons un système de clas-sification plus équitable et, finalement, nous examinons les implications de politiques d’intérêt public. There is strong relationship between birthmonth and the chance that a Canadian minor hockey player will play at an elite level. Players born in the early months of the year have an advantage. This is generally attributed to the slotting system: the way in which minor hockey groups players into age divisions. In this paper we first review the evidence. We then argue that there is more to the explanation of this relative age effect than just the slotting system; it also depends on early streaming (i.e., the partitioning of players into representative and house league teams). We suggest a more equitable slotting system, and finally, we discuss public policy implications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".