Bibliographic record
Abstract
The right to host sport mega-events is bid on by cities and countries with the expectation that tourists, money, and prestige will follow. For better or for worse, the economic and social impacts of these events on the hosts are significant. International organizations award these events, with little risk and much benefit to themselves. Unfortunately, it is not always clear how international organizations ultimately decide which hosts are chosen for this high honour and even higher burden.International sporting organizations have various bidding processes for the right to host their premier events. Each process has its drawbacks, with an overall lack of objective criteria and transparency. These problems can lead to negative effects such as decreased competition, selection of sub-optimal bids, uninformed decision-making and concerns with corruption.While there is no “one size fits all” solution, there are “best practices” conducted by many organizations that can be adopted to create a more transparent and efficient bidding process. Overall, bidding processes should: (1) be written; (2) not be subject to easy alteration; (3) account for regional balance; (4) be accompanied by a published technical report; and (5) have a viable “exit strategy” in case of host failure. These modest steps will enable potential hosts to feel that the process is fair, and will enable the organizations to ensure they are getting the most qualified host.
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 | 0.059 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".