Toronto: facing challenges, grasping opportunities
Bibliographic record
Abstract
Over recent decades, the Toronto region has experienced one of the highest rates of population growth among OECD metropolitan regions, making it one of the economic engines of Canada. With more than 5 million inhabitants, the region generates almost a fifth of the GDP of Canada as a whole, and concentrates 40% of the nation’s business headquarters. This accelerated expansion has not come at the expense of quality of life: Toronto retains its reputation as a good place in which to live. With the implementation of the Canada-US Free Trade agreement in 1989, and thanks to its strategic geographical location only a 24-hour drive from 40% of the US population, Toronto firms have successfully penetrated US markets, boosting its exports and integrating into the North American automobile production system. Toronto’s diversified regional economy, which includes a number of globally competitive clusters in finance, automobile and life sciences, as well as other prosperous and dynamic sectors in entertainment and communication technologies, has benefitted from a well-educated workforce constantly refreshed by new immigrants. While the government of Canada has set in place a pro-active immigration policy, it is the Toronto region that welcomed 40.4% of the immigrants who arrived in the country from 2001-2006. Unlike immigrants in many other large cities in the world, most newcomers to the Toronto region are highly skilled.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".