Anticipating the next move: Comparing mid-older adult master athletes and chess players on their expectations regarding aging
Bibliographic record
Abstract
Master athletes are often touted as the "gold standard" of healthy aging; however, there is a paucity of research concerning Master athletes and their expectations regarding biopsychosocial facets of the aging process. Positive expectations of aging have been associated with engaging in preventative health behaviours and lifespan longevity, and while previous research has suggested that Master athletes may actively resist negative age-stereotypes, no research has quantified and compared their expectations of aging. The present study investigated expectations regarding aging (via the 12-item Expectations Regarding Aging survey; ERA-12; Sarkisian et al., 2005) of Master athletes (n=103) and mid-older adult chess players (n=42) aged 50 years and greater. Results revealed that Master athletes had significantly better expectations of aging compared to those of chess players (overall ERA-12, p=0.0001; physical expectations, p=0.001; psychosocial expectations, p=0.001; cognitive expectations, p=0.0001). Furthermore, Master athletes engaged in significantly more healthy behaviours compared to chess players (e.g., more physically active, sexually active, less sedentary). Age split analyses of variance between middle age (50-64 years) and older age (65+ years) additionally revealed that both middle and older aged Master athletes had significantly better ERA-12 scores compared to middle and older chess players (p
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".