They have a league of their own, should they play in it?
Bibliographic record
Abstract
Guest: Toronto Star columnist Dave FeschuckBetween the professional hockey leagues, a new professional soccer league and the new franchise in the professional basketball league, women's sports is flourishing in Toronto. Girls sports too - especially hockey, where enrolment of young girls is single-handedly driving growth in the sport. Today's girls, at the elite level, face future prospects their grandmothers could only have dreamed of, but that also means they face a choice: should they continue to play on teams with boys, in leagues dominated by boys? Or should they take advantage of the many girls leagues Ontario has to offer.Dave Feschuk and Kerry Gillespie recently wrote about that issue for the Star, and Feschuk joins host Edward Keenan (coach of a girls hockey team) to discuss the factors involved in making that choice, including where the strongest competition is, the potential value of playing with body contact, the social dimensions of the sport, and the avenues that exist to national or college teams.PLUS: Special guest Irene Keenan, the host's 16-year-old daughter, talks about her own experience playing alongside boys and in all-girls environments as both a hockey and baseball player.This episode was produced by Julia De Laurentiis Johnston, Ed Keenan and Paulo Marques.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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; both teacher heads 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".