The Future Composition of the Canadian Labor Force: A Microsimulation Projection
Bibliographic record
Abstract
This article charts the future transformations of the Canadian labor force population using a microsimulation projection model. The model takes into account differentials in demographic behavior and labor force participation of individuals according to their ethnocultural and educational characteristics. As a result of a rapid fall in fertility, the Canadian population is expected to age rapidly as baby boomers start to retire from the labor market in large numbers. In response to declining fertility, Canada raised its immigration intake at the end of the 1980s, and immigration is now the main driver of Canadian population growth. At the same time, immigrants to Canada are becoming more culturally diversified. Over the last half century, the main source regions have shifted from Europe to Asia. Results of the microsimulation show that Canada's labor force population will continue to increase, but at a slower rate than in the recent past. By 2031, almost one third of the country's total labor force could be foreign‐born, and almost all its future increase is expected to be among university graduates, while the less‐educated labor force is projected to decline.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".