Directions for 21st Century Lifelong Learning Institutes: Elucidating Questions from Osher Lifelong Learning Institute Studies
Bibliographic record
Abstract
The literature regarding lifelong learning is robust, while the literature on lifelong learning institutions, centers, and programs remain under-researched in comparison. This article draws insights from a specific network of lifelong learning institutes with a rich history and high rapport in the United States: the Osher Lifelong Learning Institute (OLLI) network. Sixty articles regarding OLLIs are catalogued and highlighted to elucidate twelve thematic areas and twelve questions for future research and practice. In particular, these themes are related to adult education, healthy aging, and educational gerontology. The article concludes by reflecting on trends in and needs for institutional research and practice. Les publications portant sur l’éducation permanente sont nombreuses, contrairement à celles touchant les institutions, centres et programmes d’éducation permanente qui, en comparaison, demeurent généralement mal connus. Cet article recueille des idées d’un réseau d’instituts d’éducation permanente ayant un passé riche et de bons rapports aux États-Unis : le réseau Osher Lifelong Learning Institute (OLLI). Soixante articles portant sur OLLI ont été catalogués et analysés pour faire ressortir douze thèmes et douze questions pour la recherche et la pratique à l’avenir. Ces thèmes se rattachent à l’éducation des adultes, le vieillissement sain et la gérontologie éducative. L’article se termine par des réflexions sur des tendances et des besoins relatifs à la recherche et la pratique institutionnelles. Mots clés : recherche institutionnelle, éducation permanente, ainés, éducation des adultes, universités adaptées aux personnes âgées
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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 | 0.045 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.027 | 0.042 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".