Aboriginal Sport in the City: Implications for Participation, Health, and Policy in Canada
Bibliographic record
Abstract
The 2014 Olympic Games in Sochi, Russia have just ended, and I'm taking a moment to reflect on a pattern I've noticed over the past several Games. As the Director of the International Centre for Olympic Studies at Western University in London, Ontario, it's my job to watch the Games as they unfold in real time and to provide media with commentary and insight on whatever producers deem to be newsworthy items. Well, I don't really "watch" the Games so much as I follow news about them, mostly online, and monitor the trends in reporting. It's my responsibility to influence the type of information that gets relayed to the public by educating journalists on the issues behind their stories. For instance, the estimated $3 billion USD that Putin spent on security for the 2014 Olympic and Paralympic Games to minimize the threat of terrorism also helps to legitimize the control of civilians by making sure peaceful protestors don't disrupt the biggest party in the world by inserting non-sporting narratives, like Native rights and LGBTQ 1 issues, into the public realm. The increased use of advanced surveillance systems and military force to control the public at the Olympic Games is a new phenomenon, and a frightening one at that, for the way people appear to be willing to give up important freedoms in exchange for a massive celebration organized around athletic competitions that leave mostly unused venues and huge public debt in their place. Most journalists understand these patterns when they are given an opportunity to discuss and digest them, but claim there are limitations to what they can say or write, especially if they are working for Olympic broadcasters, which go heavy on sports reporting and light on analysis. All too often, commercial interests trump the need for information.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.032 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".