On the Meaning of Aging and Ageism: Why Culture Matters
Bibliographic record
Abstract
Like any form of discrimination, ageism does not exist in a void; it is expressed through cultural values and social beliefs. Some studies show that ageism intersects with other discriminatory attitudes, including those based on race or culture, leading to negative outcomes. However, the way older individuals, who are members of diverse cultural groups, experience and acknowledge age-based discrimination and react to ageist stereotypes may also be culturally dependent. The purpose of this paper is to further explore perceptions of aging and ageism among cultural groups of older adults in Canada. Findings from group discussions conducted among Chinese, Arab, and South Asian Indian older adults reveal that seniors living in Canada share relatively positive perceptions of aging and maintain their physical and psychological well-being, in part, because of their family and community engagement. Participants highlighted the respect that is offered to older adults in their culture and, in most cases, were grateful for their families and the policies supporting older adults in Canada. While participants were often not familiar with the term “ageism,” they had experienced a few instances of age discrimination, especially in the workplace. Results suggest that participants’ identities as older people may prevail over identities related to culture. As Canada’s society ages and becomes more diverse, these findings shed light on how culture influences the experience of aging and 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".