e-brief Fixing a Persistent Problem: Canada’s Regional Pockets of Unemployment
Bibliographic record
Abstract
National average unemployment figures, while low, continue to mask important regional differences.1 Pockets of unemployment persist, particularly in the Eastern provinces, showing that the benefits of a strong national labour market are not equally shared across regions. Over time, technological change, shifting needs for certain skill sets, and changing global demand for Canadian exports highlight the need for flexibility in our labour markets. Workers may need to regularly upgrade their skills and relocate to where jobs are available. Failure to respond to continuous change will leave Canada in a growing geographic divide, with acute labour shortages in some regions and excess supply persisting in others.2 Ontario represents the fulcrum of this imbalance. To the west, employment rates are high, and in some areas the demand for labour is greatly outstripping supply – unemployment bottoms out at 1.9 percent in southwestern Manitoba. Towards the Atlantic, unemployment reaches a national high of 15.7 percent in northern Newfoundland. The imbalance in regional labour markets underscores a misallocation of workers that reduces overall economic output. Plus, in regions with high unemployment, if individuals ’ repeated efforts to find work go unfulfilled, it can have adverse affects on personal health. To minimize this imbalance and improve Canada’s overall adaptability to economic I N
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.087 | 0.007 |
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".