Bibliographic record
Abstract
Competitive balance in sport is a desirable state for leagues to aspire to attain. Indeed, uncertainty of outcomes (Kringstad & Girginov, 2018) have been noted to be desirous. At the highest levels of professional sport, measures to promote competitive balance (e.g., amateur drafts, revenue sharing) are commonplace. It has been noted before (i.e., Wigfield & Chard, 2018) that competitive balance is similarly sought after in competitive youth sport settings. Here, the current study was completed to assess the competitive balance in various associations competing in Ontario’s boys’ representative minor hockey system. Results from the provincial championships since the 2005-06 season were analyzed for age groups ranging from U11 to U20 and across all competition levels (i.e., A, AA, AAA) by conducting a chi-square test of independence. Findings indicate that the Greater Toronto Hockey League (GTHL) dominates Ontario rep hockey across all age groups and competition levels—with few exceptions. This article offers an innovative solution to roster construction that Ontario minor hockey associations can implement to combat the GTHL’s dominance.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".