Effects of population aging on labour market flows in Canada: analytical issues and research priorities. HISSRI Working Paper 2003 A-02
Bibliographic record
Abstract
Changes in the mix of goods consumed, and changes in the age mix of the labour force are the two main channels via which population aging is likely to affect labour market flows in Canada. In this paper I argue that research should focus primarily (though not necessarily exclusively) on the second of these channels, because these effects are more direct and will occur earlier. Promising research directions in the area of labour force aging and worker flows include descriptive research on trends in labour market flows by age and education; research on the consequences of mobility (across jobs, employers, occupations, industries and regions) for older workers; understanding the effects of Canada's multifaceted income support and retraining system on older job movers, especially older job losers; studying the cost-effectiveness of retraining for older workers and the optimal targetting of such retraining; defining and identifying age discrimination in employment and the effects of public policy thereon; understanding the implications of downsizings and plant closures for older workers; and improving our understanding of the way a firm's age structure affects its behaviour and performance. In addition, research that helps assess the relative importance of some of the key processes generating labour market flows in the first place--for example, on-the-job search versus creative destruction processes-- would improve our understanding of the effects of aging on labour flows as well.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".