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
Over the last several years, we have seen an increasing recognition that many sporting activities now present the possibility of adverse health outcomes that sit well at odds with any idea of population fitness and wellbeing. As a matter of fact, the discourse regarding the implications for a number of contact sports, in particular the costs and possible trade-offs regarding neurological function and deficit, is now prevalent in terms of the wider media and academic fields. Whilst much emerging evidence and testimonies from athletes, sports people, and their loved ones outlines the damage and consequences that are immediately or longitudinally being experienced (with corresponding lessening of quality of life, or increased mortality risks), there is still an appetite for ‘dangerous’ (i.e. combat, contact, and adventure) or ‘risky’ sports, both in terms of participation and consumption. In this commentary, because of the emerging evidence demonstrating that some sports directly lead to an accelerated development of neurodegenerative syndromes, we argue that a more robust classification system should be used to create a distinction from the largely undefined categorisation of ‘dangerous’ sports, and propose an outline for what we see as ‘hazardous’ sports to reclassify those whose very rules, remit, and objectives present an identifiable risk of harm.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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