Critical Role of Social–Cognitive Age Representations
Bibliographic record
Abstract
Abstract In 2016, Statistics Canada released the results of the most recent census. For the first time ever, the proportion of Canadians aged 65-plus years surpassed the proportion aged 15 and under. The increase in the proportion of older adults was viewed as further evidence of the faster rate of aging of Canada’s population. Such demographic shifts are not unique to Canada; many industrialized nations around the world are experiencing similar changes in their populations. Increases in the older adult population in many countries might produce beneficial outcomes by increasing the potential for intergenerational contact and exposure to exemplars of successful aging. Such positive intergenerational contact could counter prevailing age stereotypes and improve intergenerational relations. On the other hand, such increases in the number of older adults could be viewed as a strain and potential threat to resources shared with younger age groups. The possibility of increased intergenerational conflict makes it more important than ever before to understand how older adults are stereotyped, how those stereotypes can produce different kinds of biased behavior toward them, and what the impact of those stereotypes are on older adults themselves. Social-cognitive age representations are complex and multifaceted. A common stereotype applied to older people is one of warmth but incompetence, often resulting in paternalistic prejudice toward them. However, such benevolent prejudice, characterized by warm overtones, can change to hostile bias if older adults are perceived to violate prescriptive norms about age-appropriate behavior. In addition to coping with age prejudice, older adults also have to deal with the deleterious effects of negative age stereotypes on their day-to-day function. Exposure to negative aging stereotypes can worsen older adults’ cognitive performance in a number of contexts. As well, age stereotypes can be incorporated into older adults’ own views of aging, also leading to poorer outcomes for them in a variety of domains. A number of interventions to counteract the effects of negative aging stereotypes appear promising, but more work remains to be done to reduce the impact of negative aging stereotypes on daily function in later life.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".