Bibliographic record
Abstract
he unemployment rate is a well-known baro- meter of labour-market health. The rise in the national unemployment rate in the years immediately following the high-tech meltdown has been replaced by sustained annual declines, resulting in a rate of 6.3% for 2006. This is not only below the 6.8% registered during the boom, but a 30-year low as well. 1 Of course not all parts of the country have shared equally in the improvement. Some have done better, others worse. Normally, comparisons involve the 10 provinces or 5 regions of Canada, but within each, many distinct labour markets can be found. This article focuses on the 28 census metropolitan areas (CMAs) and the 10 provincial non-CMA areas (see Data source and definitions). Using the Labour Force Sur- vey (LFS), the article first tracks unemployment rate dispersion for local labour markets (CMAs and non- CMA areas) between 2000 and 2006. It then examines the comparative labour market performance of these areas based on unemployment rates and rankings, and unemployment duration. Unemployment levels, labour force, and employment are provided in an appendix. Unemployment rate dispersion rising The impressive performance of the national unem- ployment rate in recent years hides considerable geo- graphic disparities. For example, in 2006 the unemployment rate in the Quebec CMA averaged 5.2% compared with 8.4% in nearby Montreal. Simi- larly, the unemployment rate in Kitchener (5.2%) was much lower than in Windsor (9.0%). That the unemployment rate will differ by geographic area is generally understood. All things being equal, the dispersion is expected to narrow in periods of eco- nomic growth, when the national rate is usually falling
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".