Bibliographic record
Abstract
Establishing the Athlete Welfare programme at the Olympic Games. I worked on this for many years—to see it implemented in Rio in 2016 was a real career highlight for me. I hope this will protect athletes in the future, and prevent harassment and abuse in all sports! …. Don’t ask!! ☺ Definitely the 2008 Olympic Games when I watched the inaugural 10k marathon swim in Beijing. I witnessed a disabled swimmer—Nathalie du Toit from South Africa—compete in the able-bodied event. Her courage and strength were inspiring. While I could say some of the studies I have conducted, papers I have authored or the Olympic Games I have worked at, my most valuable contribution to the field is (hopefully) the help and care I give on a daily basis in my clinic to athletes of all shapes, sizes and abilities over the past 30 years. The numerous athletes over the years that overcome adversity to strive for improvement and to reach personal goals—athletes who have overcome profound physical injury and emotional abuse issues. Their courage and fortitude make them all heroes in my eyes. Perseverance, resilience and creativity. Wait—that is three skills; I guess another skill is not following exact directions! Saying ‘yes’ when opportunities came my way. Although, in reality, I had to make some of my opportunities …
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.446 | 0.214 |
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".