Bibliographic record
Abstract
One of the questions I like to ask my undergraduate kinesiology students is how one becomes a sports medicine doctor. Many of them aspire to this goal but few can articulate how to get there or identify the full scope of its wide-ranging and ever-growing activities. Like other medical specialties, sports medicine is interested in both the prevention and cure of disease, sickness, and injury, but it is also rather different for it has no identifiable hospital base and in practice it is a highly diverse, multi-practitioner, multi-disciplinary, multi-specialty activity, which includes general practitioners, surgeons, gynaecologists, orthopaedists, paediatricians, dieticians, physiotherapists, masseurs, rehabilitation therapists, physiologists, exercise scientists, psychologists, chiropractors, members of the armed forces, physical education teachers, coaches, athletic trainers, and a variety of others. Furthermore, it is interested (sometimes too interested say the courts) in enhancing as well as repairing the athletic body. Indeed, the alliance between medical science and high-performance sport has become a unique and increasingly controversial adventure in the history of the human species.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.076 | 0.050 |
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".