Fostering Positive Views About Older Workers and Reducing Age Discrimination: A Retest of the Workplace Intergenerational Contact and Knowledge Sharing Model
Bibliographic record
Abstract
Ageism toward older workers is prevalent in the labor market. The present study aimed to understand psychosocial mechanisms that may counteract this form of discrimination and help retain workers in the labor force. Using a sample of 500 Canadian younger and older workers, this study tested a model hypothesizing that intergenerational contacts and knowledge sharing practices can reduce ageist views about older adults and age-based discrimination against one's own group, and in turn, enhance work engagement and intentions to remain in the workplace. The final model shows that knowledge sharing practices mediate the relationship between intergroup contacts and positive views about older workers as well as age-based discrimination. It also suggests that low levels of age-based discrimination increase work engagement and intentions to remain in the organization for workers of all ages. Practice and policy implications are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".