The Ambivalent Ageism Scale: Developing and Validating a Scale to Measure Benevolent and Hostile Ageism
Bibliographic record
Abstract
Purpose: Much like sexism, ageism is a multifaceted prejudice; it involves benevolent and hostile attitudes toward older adults. There are many scales designed to measure hostile ageism, yet none dedicated to measuring benevolent ageism. In the current studies, we developed and validated a 13-item measure: the Ambivalent Ageism Scale (AAS). Design and Methods: We employed four stages of scale development and validation. In Stage 1, we created 41 benevolent ageist items adapted from existing ageism measures. In Stages 2 and 3, we further refined the pool of items through additional testing and factor analysis and retained nine items loading strongly on two factors related to benevolent ageism: cognitive assistance/physical protection and unwanted help. In order to enable researchers to contrast benevolent and hostile attitudes, we then added four hostile ageist items. In Stage 4, we assessed the test-retest reliability of the 13-item scale. Results: The AAS had good test-retest reliability (r = .80) and good internal consistency (α = .91). As predicted, the benevolent and hostile ageism subscales differentially predicted attitudes toward older adults: higher scores on the hostile subscale predicted lower competence and warmth ratings, whereas higher scores on the benevolent subscale predicted higher warmth perceptions. Implications: The AAS is a useful tool for researchers to assess hostile and benevolent ageism. This measure serves as an important first step in designing interventions to reduce the harmful effects of both hostile and benevolent ageism.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".