DO BELIEFS THAT OLDER ADULTS ARE INFLEXIBLE SERVE AS A BARRIER TO RACIAL EQUITY?
Bibliographic record
Abstract
Abstract Past research has demonstrated that older adults are stereotyped as less malleable than younger adults, such that older adults are perceived to be less capable of changing their beliefs and learning new things. Moreover, beliefs that people are less malleable are associated with lower confrontations of prejudice, as perpetrators are seen as less capable of changing their (prejudiced) behavior. The aim of the present research was to integrate these lines of research to demonstrate that endorsement of ageist beliefs that older adults are less malleable will lead to lower confrontation of anti-Black prejudice espoused by older adults. Across four experimental studies (N = 1,310), people were less likely to confront anti-Black prejudice espoused by an 82 year-old compared to a 62, 42, or 20 year-old, due, in part, to beliefs that older adults are less malleable. Further exploration demonstrated that malleability beliefs about older adults were held across young, middle-aged, and older adult samples, though older participants were the least likely to confront prejudice, regardless of perpetrator age. Alternative mechanisms are explored, including respect, perceived social influence, and perceived awareness of egalitarian norms. These findings demonstrate how stereotypes about older adults can impede racial equality and highlight that interventions geared towards reducing ageism could, in turn, lead to greater racial equality.
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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 | 0.011 |
| 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) | 0.004 | 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".