Aging and Stereotype ThreatDevelopment, Process, and Interventions
Bibliographic record
Abstract
Age stereotypes are widespread and, although they contain some positive elements, they are primarily negative. It is likely that age stereotypes become internalized at an early age, only to negatively impact individuals when they themselves grow old. Negative views of aging can operate either explicitly or implicitly, affecting both physical and cognitive health. Thus, it is not surprising that older adults, like many other negatively stereotyped groups, experience stereotype threat. In the case of age-related stereotype threat, consequences have been observed primarily in the domain of memory. Similar to stereotype threat effects among other groups, domain and group identification moderate age-based stereotype threat effects. In addition, task demands, memory self-efficacy, and age (young-old vs. old-old) also determine who is most affected by stereotype threat. In terms of mediators, a unique set of mechanisms including lowered performance expectations and disrupted strategy use help explain how stereotype threat decreases memory performance in older adults. Initial work on interventions to combat the negative effects of aging stereotypes has shown some promising results with respect to intergenerational contact and exposure to positive aspects of aging. Although we have learned much about the effects of negative aging stereotypes on older adults, further research is required to determine the breadth of stereotype threat effects across domains, pinpoint which mechanisms best account for these effects, and test the efficacy of a wider variety of interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".