Bibliographic record
Abstract
Such is the power of the International Olympic Committee (IOC) that they require sovereign states, eager for key cities to host future editions of the Olympic Games, to enact specific legislation designed to protect, amongst other things, the Olympic brand and its associated trademarks; it is also intended to prevent the occurrence of ambush marketing before and during the period of the Games, thereby protecting the commercial interests of the IOC and the sponsors participating in the Olympic Partner program. However, there are no such requirements for guarantees pertaining to the intended legacy outcomes of these Games, either in terms of their physical manifestation, or with regard to their sustainability, despite sustainable development being one of the pillars of the Olympic Movement. This paper argues that if the IOC were serious in their professed intent that Games’ legacies be beneficial for the residents of host cities, regions and countries over time, they could require the enactment of straightforward legislation guaranteeing planned and sustainable outcomes. Furthermore, the paper provides the example of a Canadian educational foundation to illustrate the infrastructure the IOC could impose on host cities to ensure Games’ outcomes are sustainable in terms of the lifespan of venues, infrastructure, facilities and equipment.
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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".