Retirement decisions in the context of the abolishment of mandatory retirement
Bibliographic record
Abstract
Purpose The purpose of this study is to use the theory of planned behavior to test a structural model of retirement timing intentions of older workers in Canada following the abolishment of mandatory retirement. Design/methodology/approach A survey of 281 working individuals was conducted in order to test a model of retirement timing. Findings The model was a good fit to the data. Attitudes toward people at work predicted people's attitudes toward work. Attitudes toward work predicted age and life perceptions. Age and life perceptions predicted control. Control predicted social/policy influences, and finally social/policy influences predicted planned retirement age. Research limitations/implications The main limitations of this study were that the authors tested a model based on self report data. Furthermore the data were correlational therefore they cannot make causal inferences. Practical implications Work attitudes predict people's own perceptions of their life and age. And these are predictive of norms. Organizations need to consider people's perceptions of their work, if they are to retain workers past the normal retirement age. Implementing work practices/policies, e.g. flexible work, become key considerations for these organizations. Originality/value The authors now have empirical support for the contention that norms are important for investigating the short term effects of lifting mandatory retirement, but also when considering the long term effects that changing mandatory retirement policies may have on individual's retirement timing. Furthermore, they have a more comprehensive model of retirement timing.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".