Extending the Labour Market Indicator to the Canadian Provinces
Bibliographic record
Abstract
Calculating the labour market indicator (LMI) at the provincial level provides useful insights into Canada’s regional economies and reveals differing trends in the state of underlying labour market conditions across provinces. Conclusions based on the Canadian LMI do not necessarily translate to the provinces. In most cases, the correlations between the provincial LMIs and the underlying labour market variables have the expected sign. Differences among provinces reflect idiosyncratic differences among provincial labour markets. The values of the provincial LMIs are not invariant to the sample period used when constructing them. We find that using a longer sample estimation period improves the properties of some of the provincial LMIs. Recent values for the LMI show that labour markets have deteriorated notably in Alberta, Saskatchewan, and Newfoundland and Labrador. At the same time, the LMIs for British Columbia, Ontario, Quebec and New Brunswick have improved over the course of the past year and the gap between the unemployment rate and the LMI has tended to narrow.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".