Bibliographic record
Abstract
Mega-sporting events (MSE) are increasingly demonstrating the capacity to leave a human rights and anti-corruption legacy: a series of human rights and anti-corruption norms, standards, practices and laws, which have application beyond the event, are likely to remain in place after the event is over, the adoption of which is accelerated by hosting an MSE. If we were to measure the difference between where a country began in relation to these issues and where the nation was on the eve of the event – or, put another way, the extent to which a country leveraged the MSE to effectuate enduring national reforms – we would find success in an unexpected place. Although Qatar has notoriously failed to effectively address many human rights issues, it has reformed its national labour rights framework to an uncommon and underappreciated degree. The subsequent FIFA World Cup hosts in 2026 – Canada, Mexico and the United States – have promised to leave both human rights and anti-corruption legacies. For MSE governance and global human rights and anti-corruption movements generally, we should acknowledge and support these developments. Failure to do so raises the spectre of what the literary theorist Edward Said once called ‘orientalism’.
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 | 0.000 | 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 | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".