Intersectional Ageism: Prescriptive Stereotypes and Moral Judgments of Older Adults
Bibliographic record
Abstract
Abstract Research on ageism has primarily focused on descriptive stereotypes, while prescriptive expectations—societal beliefs about how older adults should behave—have received less attention, particularly in relation to intersecting identities. When older adults do not conform to these expectations, they may experience negative social consequences, and drawing from intersectionality research, these consequences are likely to vary among older adults with different intersecting identities. To examine these dynamics, we surveyed 355 young adults on societal pressures placed on older adults with intersecting identities. Using a modified Succession, Identity, and Consumption scale (North & Fiske, 2013), we varied the target’s race (Black, White) and gender (man, woman). In a separate analysis, participants also rated the perceived immorality of these targets in society. We found that succession-based prescriptive stereotypes—specifically the expectation that older adults should make room for younger generations—varied by intersectional identity, such that older White men were most strongly expected to adhere to this stereotype. A separate analysis of moral violation ratings revealed that perceptions of immorality also varied across intersectional targets, with older White men rated as the greatest moral violators in society. These findings demonstrate that prescriptive age stereotypes and moral judgments are shaped by intersecting identities. Older White men were most strongly expected to step aside for younger generations and were also rated as the greatest moral violators in society. This highlights the need for further research on how intersectional biases shape perceptions of aging and the distinct social consequences older adults face in different domains.
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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 | 0.013 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".