Bibliographic record
Abstract
As a 12-year-old living in Moscow in 1973, I watched Canada's Glenda Reiser win the 1,500m at the World University Games, and, as children do, decided that would be me. Raised to believe I could do whatever I set my mind to, off I went. But the journey was to be far more complicated than I could ever have anticipated. Each time I felt I was making progress in my quest for excellence, a barrier would appear. I felt like Sisyphus who was sentenced to roll a rock up a hill. Each time it reached the top, the rock rolled back down again. His crime had been to challenge Zeus. Mine was much less grandiose: I wanted only to reach my maximum potential free of restrictions created by the sport system. Athletes are still rolling their rocks up hills. They shouldn't be. The role of the system should be to flatten the barriers, to ease the way, to widen the path. Unfortunately, much of the time that is not what happens. But it could be, and what follows is the story of an effort to help athletes get to the top of the hill a little more easily, with less interference from the system. My thesis is a simple one: athletes want to perform. Their performance, and the supports needed to create it, should be the focus of the sport system. To ensure that focus, athletes and their coaches must get involved. If, as athletes, we do not shape and support the systems we need, we will roll our rocks for evermore.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.066 | 0.030 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.022 | 0.065 |
| Insufficient payload (model declined to judge) | 0.008 | 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".