Moving through Toronto’s PATH: Assembling private urban governance
Bibliographic record
Abstract
This paper explores Toronto's urban PATH, a 30 km network of underground pedestrian tunnels and elevated walkways that connect shopping areas, residential towers, mass transit and downtown destinations. Both as a case and heuristic, this paper situates Toronto's PATH as an assemblage of private urban governance forms, exploring emergent and evolving constellations of power and responsibility for governing city space that defy easy distinctions of 'public' or 'private'. As an urban assemblage, the PATH comprises potential and actual entities and associations, and is an accumulation of encounters. Never a stable or static entity, the PATH and its governance, we argue, is provisional, revealing constantly evolving connections, alignments and political-economic potentialities. We contend the PATH serves as a palimpsest of mutating governing relations; a multiplicity of meanings, visions and encounters etched into the built environment. By focusing on public and private vestiges, wayfinding, and visibility, and private verticalising ventures, we highlight how practices, logics, processes, urban actors and their histories collide to form fragile, provisional urban alignments and visions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".