Transportation: The Bottleneck of Regional Competitiveness in Toronto
Bibliographic record
Abstract
The reconciliation of global trade interests, regional transportation necessities, and urban everyday life and politics is at the centre of the emergence of new infrastructures in general, and new transportation networks in particular. In Toronto recent rescaling and restructuring of regional government provide a potential opportunity for new modes of regional governance. Will transportation in the Toronto region remain bifurcated into a premium network of transport infrastructure systems, on the one hand, and underserved community-based systems, on the other hand? We argue that the existing transportation situation has become a bottleneck for the continued globalization of the region, because global and local circuits of mobility are not well coordinated and various scales of decision making do not visibly interact for the regional good. At this point we ask whether there is an emerging collective actor (or collective actors) to remedy the situation, or whether we can instead expect the anarchic governance model of the recent past, with its biases towards suburban road building, to continue. We are particularly interested in casting light on the institutions that have been created (or dismantled) to deal with comprehensive transportation planning in the region. We posit that, in the context of recent institutional reforms, transportation agencies had to adjust, and we pose a series of research questions as a means of exploring and understanding those adjustments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".