REACTIONS TO PERPETRATORS OF BENEVOLENT AGEISM: WHAT INFLUENCES PERCEPTIONS?
Bibliographic record
Abstract
Abstract Ageism against older adults is a longstanding and pervasive problem. Benevolent ageism can be particularly difficult to address, given it is characterized by warmth and condescension. In two studies, we examined the impact of confronting prejudice on perceptions of a young adult perpetrator who expressed benevolent ageism. In both studies, the perpetrator initiated unwanted help to an older person who was either 62 or 82 years old, and the older adult either accepted or politely rejected the unwanted assistance. In both studies, participants felt that rejecting (i.e., confronting) the ageist behavior decreased the likelihood the perpetrator would repeat the behavior. Young adult participants used the age of the older target as a cue for interpreting the perpetrator’s intentions, such that they rated the perpetrator’s actions as less intending to offend if the older target was 82 than 62 years of age. Moreover, younger adults used the age of the older target to determine whether the perpetrator should be confronted, indicating that confrontation should occur when the target was 62 vs. 82 years old. Taken together, these studies demonstrate that participants of all ages feel that confronting benevolent ageism can be an effective tool for reducing biased behavior. As well, these findings show that the age of the older adult target might be used as a normative cue to interpret the perpetrator’s intentions and actions, thus providing a more nuanced picture of what might drive witnesses’ responses to ageist behavior.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".