Expertise and aging: maintaining skills through the lifespan
Bibliographic record
Abstract
Abstract As lifespan continues to increase in many developed countries, so too does the age at which we see extraordinary achievements from older adults. Examples from running, golf, and other domains continue to redefine what is possible as we age. Evidence suggests, however, that progression through adulthood is associated with a dramatic decline in all manner of physical and cognitive abilities, from physiological capacities (e.g., VO2 max) to cognitive and perceptual functions (e.g., IQ scores, reaction time). In the face of such precipitous decline in specific abilities, how do we account for maintenance of skilled performance and expertise amongst those supposedly well along the age-decline curve? Expert performers are seemingly able to sustain high levels of achievement in the face of an overall deterioration in general capacities. Moreover, experts maintain this performance in spite of reduced involvement in their field. There are three primary explanations for the ability of experts to maintain superior performance in spite of an overall decline in abilities: (a) preserved differentiation, (b) compensation, and (c) selective maintenance. Overall, research into the high achievements of older adults may reveal a great deal with respect to skill preservation and how to best counter age-related decline.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".