CALLING OUT AGEISM: HOW INTERSECTING IDENTITIES AND CONFRONTATION TONE AFFECT PERCEPTIONS OF OLDER CONFRONTERS
Bibliographic record
Abstract
Abstract The few studies that have examined confrontations of ageism have shown that older adults are viewed differently after challenging the behavior. What remains unclear is how older people’s intersecting identities, as well as their confrontation tone, might influence reactions to their countering ageism. To address this question, we surveyed 500 young and older adults regarding their perceptions of warmth and competence towards older adult confronters who varied by race (Black, White) and gender (man, woman). Participants evaluated the older individuals both before and after they either moderately or strongly confronted an ageist backhanded compliment (“You don’t look that old”). The older confronter’s race and their response to the ageist comment moderated how they were perceived by participants. Specifically, both young and older adult participants rated older Black confronters as warmer when they used a moderate compared to a strong confrontation tone but did not make that distinction for White confronters. However, when the tone of the confrontation was not considered, young, and older participants generally differed in their perceptions of confronters. Older adult participants rated White confronters lower on warmth and competence after confronting compared to Black confronters. This pattern was not observed among young adult participants, underscoring the significance of intersectional identities in shaping perceptions among older adult perceivers. This study shows that older adult confronters’ intersecting identities influence how they are perceived when they challenge ageist comments. This suggests that customized confrontation techniques may be needed for older adults whose gender and racial identities vary.
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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 | 0.007 |
| 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) | 0.003 | 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".