Untangling Ageism: Insights From Diverse Perspectives, Methods of Inquiry, and Contexts
Bibliographic record
Abstract
Abstract Ageism, defined as stereotypes, prejudices, and discrimination, has been studied for over five decades now. Yet, it is still more prevalent than the other big “isms” such as racism or sexism but is less recognized than other “isms.” In this symposium, we present research conducted in various socio-cultural contexts including Canada, China, Germany, Israel, and the United States to highlight the complexity and multi-faceted nature of ageism. Levy and Martin present research from the Health and Retirement Survey showing the importance of positive views on aging in reversing and preventing the development of mild cognitive impairment. Xi and colleagues highlight the paradox of digital ageism in two different cultural contexts: the United States and China, demonstrating differential age and culture effects of digital ageism on technology use . Ayalon and colleagues examine shifts in society and their potential effects on ageism. They demonstrate a reduction in ageism following the Sward of Iron war in Israel. de Paula Couto & Rothermund examine the interaction between chronological age and disengagement norms, showing a reduction in the reports of age discrimination in those older persons who are more likely to hold disengagement norms. Finally, Gans & Chasteen discuss intersectional identities in relation to prescriptive norms, demonstrating that older White men are the ones most expected to step aside for younger generations and most negatively judged as moral violators if they fail to comply with prescriptive age norms. The studies highlight the importance of context and the distinction between prescriptive and descriptive age norms.
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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 | 0.086 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.014 | 0.070 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".