AGEISM IN EVERYDAY CONTEXTS: FACTORS THAT INFLUENCE PERCEPTIONS AND OUTCOMES
Bibliographic record
Abstract
Butler coined the term ‘ageism’ in 1969 to highlight discriminatory practices against older adults. Since then the definition has expanded to encompass age-based discrimination across the lifespan. Although much research has examined individual experiences of other forms of discrimination, e.g., racism or sexism, surprisingly little is known about the degree to which individuals face ageism in their everyday lives. It is therefore pertinent to understand how age biases manifest in the context of individuals’ daily lives, the variety of forms that ageism can take, the perceptions of acceptability of these experiences, and the outcomes that result. This symposium examines young, middle-aged and older adults’ experiences of ageism at both interpersonal and societal levels. Chasteen et al. consider how adults of all ages respond to benevolent and hostile ageism from perpetrators of varying degrees of interpersonal familiarity. Horhota et al. provide a detailed picture of adults’ personal experiences of ageism by examining age differences in the general domain (e.g., work, social) and specific content of reported ageist experiences, in addition to examining the coping strategies used to respond to the experience. Swift considers ageism in the workplace, examining negative meta-perceptions of older workers and their impact on job satisfaction and retirement intentions. Finally, North presents evidence that agency prescriptions unequally target men and women across the lifespan, and explores the social and economic consequences for agentic behavior in various domains.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".