Expertise and aging: maintaining skills through the lifespan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it