Mapping the Future of Reminiscence: A Conceptual Guide for Research and Practice
Bibliographic record
Abstract
Nearly 50 years after Butler’s seminal 1963 contribution, the field of reminiscence and life review is entering a more mature stage. Isolated examples of increasingly sophisticated studies have recently emerged that can serve as a sound, cumulative data base. However, the field lacks an overarching conceptual model describing emerging trends, neglected domains, and key linkages among component parts. In the present article, the authors selectively, yet critically, review prior limitations and promising developments and then describe a comprehensive, multifaceted conceptual model that can guide future research and practice. The authors initially situate their model within a particular theoretical orientation (i.e., life-span psychology). They then describe a heuristic model that identifies and discusses triggers, modes, contexts, moderators, functions, and outcomes. Finally, the authors illustrate how these interactive factors influence both theoretical and applied areas.
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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 | 0.029 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".