PERCEPTIONS OF PERPETRATORS OF AGEISM
Bibliographic record
Abstract
Abstract What are the consequences for perpetrators who engage in different types of ageism? We compared young (n=316), middle-aged (n=464), and older adults’ (n=273) perceptions of a perpetrator who engaged in an ageist action. Participants read a vignette about a pedestrian (the perpetrator) offering unwanted help to an older woman crossing the street. We manipulated the ageism type (benevolent or hostile), the reaction of the older target (acceptance, moderate confrontation or strong confrontation) and assessed the overall impression of the perpetrator. Main effects emerged for Ageism Type and Age Group. Overall, participants rated the perpetrator more positively in the benevolent condition compared to the hostile condition. Middle-aged and older adults rated the perpetrator more positively than young adults did. A Time x Confront interaction suggested that the perpetrator’s overall impression was not impacted when the target of the ageist act accepted the action or moderately confronted the perpetrator. In contrast, when the target confronted the perpetrator strongly, the overall impression of the perpetrator decreased. An Ageism Type x Age Group x Time interaction on overall impression also emerged. There were no age differences when the perpetrator committed a hostile act of ageism. In contrast, in the benevolent condition young and older adults perceived the perpetrator more negatively after the target’s reaction, whereas middle-aged adults did not adjust their impression. Taken together, these results suggest that young and older adults may be less accepting of benevolent ageism compared to middle-aged adults.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".