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.
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 | 0.021 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".