Bibliographic record
Abstract
INTRODUCTION The Masters sport movement, which began locally as collections of community-based ventures, has grown tremendously and expanded nationally and internationally (Hastings, Cable and Zahran 2005). It is estimated that over 50 countries hold their own Masters sport events, and that the Master phenomenon relates to at least 44 different sports worldwide (Coaching Association of Canada 2013). Masters athletes (MAs) are individuals who participate in competitive sport in their adult years, with organized events typically beginning at age 35 and extending into the 90s. Masters sportspersons are characterized by formal registration for an organization (e.g., club or league) or event (e.g., 10 km road race, a bonspiel, a Masters games), and a sufficiently regular pattern of involvement that supports their training in preparation for a sport event (Young 2011). Some adult sport participants train in programmes dedicated to their specific age-cohort, whereas others carry out their day-to-day involvement alongside younger (e.g., adolescent, young adult) athletes; however, Master sport is probably best characterized by adults’ participation in competitive events that are segregated and advertised to adults alone. Indeed, events that are dedicated and marketed to Masters sportspersons have grown tremendously in number and in popularity in recent years.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.179 | 0.024 |
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".