‘I know it exists … but I haven't experienced it personally’: older Canadian men's perceptions of ageism as a distant social problem
Bibliographic record
Abstract
ABSTRACT This paper examines how older men perceive, experience and internalise ageist prejudice in the context of their everyday lives. We draw on in-depth interviews with 29 community-dwelling Canadian men aged 65–89. Although one-third of our participants were unfamiliar with the term ageism, the majority felt that age-based discrimination was prevalent in Canadian society. Indicating that they themselves had not been personally subjected to ageism, the men considered age-based discrimination to be a socially distant problem. The men explained their perceived immunity to ageism in terms of their youthful attitudes and active lifestyles. The men identified three groups who they considered to be particularly vulnerable to age-based discrimination, namely women, older workers and frail elders residing in institutions. At the same time, the majority of our participants had internalised a variety of ageist and sexist stereotypes. Indeed, the men assumed that later life was inevitably a time of physical decline and dependence, and accepted as fact that older adults were grumpy, poor drivers, unable to learn new technologies and, in the case of older women, sexually unattractive. In this way, a tension existed between the men's assertion that ageism did not affect their lives and their own internalisation of ageist stereotypes. We consider our findings in relation to the theorising about ageism and hegemonic masculinity.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".