Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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 it