Canadian government’s framing of ageing at work and older workers: Echoing positive ageing models
Bibliographic record
Abstract
BACKGROUND: Public representations of ageing can influence how individuals perceive their own experience of ageing. Results of studies on the OECD (Organisation for Economic Co-operation and Development)'s governmental messages on older workers suggest that they are mainly constructed around economic productivity and personal responsibility. OBJECTIVE: The goal of this study is to examine how the Canadian government frames issues around ageing, work and older workers. Canada is facing a rapidly ageing workforce, hence the importance of examining how the government discusses ageing at work. METHOD: A thematic content analysis was conducted on a total of 154 government web pages. RESULTS: Results revealed that predominant themes revolve around economic challenges resulting from an ageing workforce. Older workers are depicted as a key component for the (economic) management of an ageing workforce. More specifically, older workers who intend to continue working are highly valued in the government's messages which present them as productive citizens and role models for "ageing well". CONCLUSION: Canada's response to the challenges of an ageing workforce echoes the underlying standards of positive ageing models, which may generate, perhaps inadvertently, a new form of ageism by creating intra-and intergenerational divides in the workplace.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".