Sport and Inclusion: Are Major Sporting Events Inclusive of First Nations and Other Groups?
Bibliographic record
Abstract
The following discussion is one of the five thought-provoking dialogues in the Sport and Society series (February 8th-March 15th 2010) featuring Olympic and Paralympic athletes who have used their celebrity to make a difference in the world. The series was hosted by Intellectual Muscle: University Dialogues for the Vancouver 2010 Games, developed by Vancouver 2010 and the University of British Columbia, in collaboration with universities across Canada and The Globe and Mail. Abstract: Aboriginal inclusion and participation in the Olympics have historically been ceremonial and cultural. Are there still bureaucratic, financial and social barriers preventing talented aboriginal athletes from participating in the Games? How do we overcome these barriers? Waneek shares the story of her journey and how she helps others to achieve their own dreams through sport. A panel discussion with invited participants follows her presentation. Audio only (mp3) and video recording (mp4) available.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".