Canada's unemployment mosaic, 2000 to 2006
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 The rise in the national unemployment rate in the years immediately following the high-tech meltdown has been replaced by sustained annual declines, resulting in a rate of 6.3% for 2006.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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it