Testing the Shielding Effect of Intergenerational Contact against Ageism in the Workplace: A Canadian Study
Bibliographic record
Abstract
Negative outcomes of ageism in the context of the Canadian labor market are well documented. Older workers remain the target of age-based stereotypes and attitudes on the part of employers. This study aims at assessing (1) the extent to which quality and quantity intergroup contacts between younger and older workers as well as knowledge-sharing practices reduce ageist attitudes, in turn (2) how a decrease in ageist attitudes increase the level of workers' engagement and intentions to remain in the organization. Data were collected from 603 Canadian workers (aged 18 to 68 years old) from private and public organizations using an online survey measuring concepts under study. Results of a path analysis suggest that intergroup contacts and knowledge-sharing practices are associated with positive attitudes about older workers. More so, positive attitudes about older workers generate higher levels of work engagement, which in turn are associated with stronger intentions to remain with the organization. However, positive attitudes about older workers had no effect on intentions to remain in the workplace. Results are discussed in light of the intergroup contact theory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".