Bibliographic record
Abstract
While ageism can be understood as an unconscious defence mechanism against a feeling of apprehension toward ageing on behalf of young and middle-aged groups, older persons themselves are not exempt from such internalized and implicit psychological machinations. Internalized ageism constitutes an insidious form of ageism that compels older adults to embrace social norms that devalue or marginalize same-aged peers by either acting in ways that reinforce the youth norm by battling the visible markers of ageing such as grey hair and wrinkles or denying any commonality and camaraderie with same-aged peers. This article explores that interface between internalized ageism and older adult learning by analyzing one of its hallmark institutions, the University of the Third Age (U3A), in the context of psychosocial interventions that are utilized by older people to defuse or counterbalance the noxious effects of negative self-perceptions of ageing. Research evidence demonstrated that U3A members generate counter-stereotypes by constructing a “third age” mental imagery and positioning themselves firmly in it while also practicing self-differentiation strategies to ameliorate or even prevent the negative impact of internalized ageism on their self-esteem and confidence. The U3A not only functions to meet the expressive and coping needs of older persons but also serves as a safe haven and buffer zone for older persons to stretch their middle-aged identity and at the same time distance themselves from being labelled as members of the “old age” cohort.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".