Bibliographic record
Abstract
In one legal ruling, an all female fitness club was not required to provide membership to a man who had been denied it, yet in another, a hockey league was required to allow a female player onto a boys ’ team. Such seemingly contradictory decisions can leave sport administrators scratching their heads about what might, and might not, constitute sex discrimination in sport. In Sex Discrimination in Sport – An Update, lawyer and sport management professor Hilary Findlay reviews cases since the landmark Blainey ruling to give an up-to-date legal snapshot of the ‘lay of the land ’ as it applies to sex discrimination today. Her overall finding, while no case has altered the basic position confirmed by the Blainey decision (that females have the right to participate on male teams where there were no female teams, subject only to considerations of physical safety), there have been cases since Blainey that have amplified and clarified some underlying legal issues. The result has been significant legal develop-ments in the areas of jurisdiction, justification for discrimination, reasonable accommodation and affirmative action programs in sport. The report begins with a presentation of seven different real-life discrimination scenarios that set the stage for an explanation of the subtle issues that now enter the legal analysis of discrimination. Federal and provincial jurisdiction is described, along with some of the unique circumstances of the Canadian sport
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.483 | 0.435 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".