The Future of the Canadian Industry Forecasts of Labour Markets for Selected Industrial Aggregates
Bibliographic record
Abstract
This paper aims to provide forecasts of labour supply and labour demand until 2035 for Canada and the provinces and for 29 industrial aggregates as defined by the North American Industry Classification System (NAICS). To conduct our projections, we use a simple trend-based forecasting approach, developed by the Boston Consulting Group (BCG), holding other variables constant. Then, we compare our results to various projections conducted by governments and consulting groups. We find that discrepancies between our results and those of other publications using the same methodology come down to differences in the datasets used. We also find that the provinces can be classified into three categories with increasingly deeper expected labour shortages: sparsely populated provinces, densely populated provinces and oil-rich provinces. Compared to other publications, our results do not always line up if other publication include more recent years a 2013, the last year of data used in this paper. At the industry level, we find that tightly regulated industries or industries with higher entry costs yield more consistent long-term labour market forecasts regardless of the assumptions used when compared to highly competitive industries. We conclude by noting that in small samples, our methodology fails to distinguish between actual trends and noise in the data. As a result, trend-based methodologies are probably more appropriate for very high level and general analyses.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".