Locating the Urban In‐between: Tracking the Urban Politics of Infrastructure in <scp>T</scp>oronto
Bibliographic record
Abstract
Abstract In the urban studies literature, urban politics is usually considered in two distinct locations: the city (often understood in quite conventional centralist ways) and the suburb (understood as spatially peripheral and politically at odds with the central city). At the metropolitan scale, the two types of urban politics are discussed in relation to one another. More recently, the metropolitan scale of urban politics has been expanded to regional dimensions. We pose the question of location of urban politics from a specific deficit in the geography of centre, suburb and metropolis. We argue that in today's regional political socio‐spatiality, politics will have to be found ‘in‐between’ the old lines of demarcation. Following Tom Sieverts' (2003) advice to look at the ‘in‐between’ cities that are neither old downtown nor new suburb but complex urban landscapes of mixed density, use and urbanity, we reveal the political vacuum that is at the heart of the urban region today. Using the politics of infrastructure in Toronto as our empirical example, we will show that vulnerabilities and risks for urban populations in that Canadian metropolis' in‐between city are co‐generated by the failure of conventional political spaces and processes to capture the connectivities threaded through those places that are in‐between the centre and exurbia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".