PERCEPTIONS OF INDIVIDUALS WHO CONFRONT AGEISM
Bibliographic record
Abstract
Abstract What are the consequences for older adults who confront ageism? We compared young (n=316), middle-aged (n=464), and older adults’ (n=273) perceptions of an older target who confronts the perpetrator of an ageist action. Participants read a vignette about a pedestrian offering unwanted help to an older woman crossing the street. We manipulated the type of ageism (benevolent or hostile), the reaction of the older target (acceptance, moderate confrontation or strong confrontation) and assessed how impressions of warmth, competence and overall impression of the target changed over time. Type of Ageism x Reaction x Time interactions emerged for all three variables. In the hostile condition, a strong confrontation resulted in the target being rated as less warm, more competent, and the overall impression decreased over time. In contrast, a moderate confrontation increased perceptions of warmth, competence and overall ratings of the target. In the benevolent condition, a strong confrontation decreased perceptions of the target’s warmth, competence and overall impression. Moderate confrontation increased perceptions of target competence but did not change perceptions of warmth or overall impression. Targets that accepted the ageist act were rated lower on warmth for both hostile and benevolent conditions. Competence ratings were not affected. However, targets that accepted benevolent ageism experienced a cost to their overall impression. Taken together, these results suggest that when confronting ageism, older adults should take a moderate approach. When participants perceived the target’s reaction to be incommensurate with the offer of help, the target was viewed more negatively overall.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".