Comparing young and older adults’ perceptions of conflicting stereotypes and multiply-categorizable individuals.
Bibliographic record
Abstract
Individuals can be simultaneously categorized into multiple social groups (e.g., racial, gender, age), and stereotypes about one social group may conflict with another. Two such conflicting stereotype sets are those associated with older adults (e.g., frail, kind) and with Black people (e.g., violent, hostile). Recent research shows that young adult perceivers evaluate elderly Black men more positively than young Black men, suggesting that components of the elderly stereotype moderate the influence of conflicting Black stereotypes (Kang & Chasteen, 2009). The current research begins to examine whether this pattern of perceiving multiply-categorizable individuals is maintained among older adults or altered, perhaps due to aging-related cognitive and motivational changes. In three studies using different targets and evaluative tasks, both young and older participants showed evidence of an interplay between Black and elderly stereotypes, such that they perceived elderly Black targets more positively than young Black targets. A similar pattern was observed when assessing emotion change (Study 1), making ratings of warmth and power in the past, present, and future (Study 2), and when directly comparing young and old Black and White targets on traits related to warmth and power (Study 3). The absence of age differences suggests that evaluation of multiply-categorizable targets follows comparable underlying patterns of stereotype activation and inhibition in younger and older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".