Bibliographic record
Abstract
As we celebrate 100 years of the Olympic Winter Games, it is an appropriate time to reflect on these Games and particularly on their governance. In this paper, I examine the governance of the Olympic Winter Games from a Canadian perspective and reflect on why there have been multiple failed bids and what it means for the Olympic Winter Games and their owner, the International Olympic Committee, moving forward. From the previous studies on Canadian Olympic (Winter) Games governance and documentary evidence from the more recent bid attempts, I show how the structure and processes, institutions and procedures, and multijurisdictional and multisectoral nature of the Games in Canada allowed the country, in the past, to be innovative in its governance and the actors brought onboard, such as the Four Host First Nations. However, current national realities ( e.g. , reconciliation efforts with the Indigenous communities) have challenged established (Western colonial) ways of doing and led, at least in part, to the failed bids. Yet, the Indigenous way of governing may be a light of hope for the International Olympic Committee as it struggles with a lacklustre reputation and its need to address the environmental sustainability of the Olympic Winter Games.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.037 | 0.012 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".