Bibliographic record
Abstract
There is no city in North America that has a greater opportunity to create economic prosperity for many in the way Toronto does. If we don’t keep our eye on how to take advantage of the benefits, we risk squandering that opportunity. Let’s take stock of Toronto. We’re booming. Toronto is one of the top cities in North America in the number of construction projects on the go. People are choosing to move to our city in staggering numbers—roughly 120,000 people per year make Toronto their home. We have a financial sector that is one of the most sophisticated in the world, a tech sector whose market growth is second only to Silicon Valley, and the potential to have the fifth largest human health sciences cluster on the planet within the next ten years. The challenge that Canada has, and Toronto has most acutely, is that opportunity is not shared as well as it could be. This is particularly true for our city’s underrepresented communities—particularly youth and newcomers to Canada. Toronto has an overall unemployment rate of 6.7 per cent. Unemployment rates for youth and newcomers, however, stand at an unacceptably high 18 per cent and 20 per cent respectively. At the Toronto Region Board of Trade, I work with 12,000 different business leaders, many of whom tell me they are desperate because they simply cannot fill all the positions they have available.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.019 | 0.012 |
| Insufficient payload (model declined to judge) | 0.221 | 0.058 |
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".