Learning as an Important Privilege: A Life Span Perspective with Implications for Successful Aging
Bibliographic record
Abstract
Research has demonstrated the cognitive and mental health benefits of learning new skills and content across the life span, enhancing knowledge as well as cognitive performance. We argue that the importance of this learning – which is not available equally to all – goes beyond the cognitive and mental health benefits. Learning is important for not only the maintenance, but also enhancement of functional independence in a dynamic environment, such as changes induced by the COVID-19 pandemic and technological advances. Learning difficult skills and content is a privilege because the opportunities for learning are neither guaranteed nor universal, and it requires personal and social engagement, time, motivation, and societal support. This paper highlights the importance of considering learning new skills and content as an important privilege across the life span and argues that this privilege becomes increasingly exclusionary as individuals age, when social and infrastructural support for learning decreases. We highlight research on the potential positive and negative impacts of retirement, when accessibility to learning opportunities may vary, and research on learning barriers due to low expectations and limited resources from poverty. We conclude that addressing barriers to lifelong learning would advance theories on life span cognitive development and raise the bar for successful aging. In doing so, our society might imagine and achieve previously unrealized gains in life span cognitive development, through late adulthood.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".