Intergenerational Contact Predicts Attitudes Toward Older Adults Through Inclusion of the Outgroup in the Self
Bibliographic record
Abstract
OBJECTIVES: With our rapidly aging population, it is critical to understand biases toward older people and what can be done to reduce ageism. Intergenerational contact can improve attitudes towards older people, but the effect of inclusion of outgroups in the self (IOS) in the context of intergenerational contact remains unexplored. In addition, stereotypes of warmth and incompetence may be affected differently by contact experiences and have different roles in effecting change in ageist attitudes. METHOD: In this study, we modeled the relationships between intergenerational contact, IOS, and stereotypes of warmth and incompetence in predicting attitudes towards older adults in a young community sample (n = 302; 18-30-year olds). RESULTS: We found that positive contact with one older adult reduced incompetence stereotypes both directly and through an increase in IOS, and both the increase in IOS and the decrease in incompetence stereotypes predicted better attitudes towards older adults. Incompetence stereotypes were a stronger predictor of age-related attitudes than warmth stereotypes. DISCUSSION: This suggests that interventions aimed at improving ageist attitudes through intergenerational contact should focus primarily on disconfirming incompetence stereotypes instead of merely increasing warmth perceptions, which could be done in part by increasing cognitive overlap with older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".