Employment in Ontario’s Industrial Heartland: Evidence of Economic Decline in a Mid-Sized Industrial City
Bibliographic record
Abstract
As the “second wave” of globalization commenced in the late 1970s and early 1980s, countries around the world underwent economic restructuring. Canada, as one of the nodes in an integrated global economy, was no exception. In particular, the province of Ontario, which was and still is the biggest manufacturing hub in Canada, experienced a decline in terms of manufacturing employment. Inevitably, for London, a mid-sized industrial city located in Southwestern Ontario, this transition had major implications, a reality that it shared with several other cities in the southwest. As closely integrated with the broader North American economy, London’s loss of jobs due to outsourcing and other technological and global forces was certainly not unique. Yet as demonstrated in this paper, London’s labour market has performed particularly poorly since 2001 relative to most other census metropolitan areas (CMAs) across Ontario, or for that matter, across Canada. This is true in terms of overall levels of job creation, as well as growth in the types of jobs that are generally considered desirable in the 21st century, i.e. jobs in higher skilled occupational categories.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".