The age-performance profile of professional and recreational marathon runners
Bibliographic record
Abstract
The relationship between age and productivity/performance has for decades been a matter of policy concern. However, given the massive demographic changes that most industrialized societies are confronted with over the next decades (United Nations 2013), this question needs to be addressed again, for example to design adequate pension policies because "policies on aging should take into account physical deterioration rates" (Fair 1994: 117). While early studies (e.g. Breen and Spaeth 1960; Dennis 1956; Meltzer 1949; Zuckerman 1967) have used rather small samples of either bluecollar workers or researchers to identify the age-performance gradient, more recent studies are based on large samples with detailed information on homogeneous groups of workers such as American farmers (Tauer 1995), Belgian, Canadian, and Dutch manufacturing workers (Cataldi et al. 2011; Dostie 2011; Lallemand and Rycx 2009; van Ours and Stoeldraijer 2011), and German automobile workers (Börsch-Supan and Weiss 2016). Moreover, particular groups of either artists (e.g. British novelists [Crozier 1999] and American contemporary painters [Galenson and Weinberg 2000]) or American physicists and earth scientists (Levin and Stephan 1991), psychologists (Horner et al. 1986), chemists, geologists, mathematicians, and sociologists (Cole 1979), and economists (Oster and Hamermesh 1998) as well as Australian judges (Smyth and Bhattacharya 2003) have been studied to identify an occupation-specific age-performance relationship.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".