Bibliographic record
Abstract
Abstract The human rights and anti-corruption legacy is poised to steadily progress in three future megasport hosts: Australia and New Zealand, co-hosts of the 2023 FIFA Women’s World Cup; Italy, host of the 2026 Milan Cortina Winter Olympics; and the United Bid of Canada, Mexico, and the United States, co-hosts of the 2026 FIFA Men’s World Cup. Admittedly, it remains too early for any organizing committee to have formulated substantial anti-corruption and human rights policies. Still, the bid documents, combined with early interviews with officials and stakeholders, reveal significant opportunities for advancing the concept that sports can, and should, leave human rights and anti-corruption legacies. Indeed, the concept could reach its fullest development with the United Bid’s 2026 FIFA Men’s World Cup. The United Bid announced an intention to implement a number of anti-corruption compliance and human rights due diligence measures, using the word “legacy” in conjunction with these measures. Should it deliver on these promises without dependence on the catalyzing effect of scandals, the United Bid could become the first host to declare in its bid, and then to implement, a proactive, intentional, and two-dimensional legacy.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".