Older age as a time to contribute: a scoping review of generativity in later life
Bibliographic record
Abstract
Abstract Research on later-life generativity has promoted a new view of older persons that, far from the traditional images of disability, dependence and frailty, recognises their capacities, and potential to continue growing, while underlining their participation and contributions to families, communities and society. The goal of this study was to carry out a scoping review on later-life generativity, the first one conducted on this topic as far as we know, to show how studies in this area have evolved, which aspects of generativity in later life have been studied, and the methodological and epistemological approaches that are dominant in this area of inquiry. Our scoping review shows that research into generativity in later life has grown steadily over the past 30 years, and particularly during the last decade. However, our results also show how such growing interest has focused on certain methodological approaches, epistemological frameworks and cultural contexts. We identify four critical gaps and leading-edge research questions that should be at the forefront of future research into generativity in later life, gaps that reflect biases in the existing literature identified in the study. These are classified as methodological, developmental, contextual and ‘dark-side’ gaps.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".