Failure When Fragmented: Public Land Ownership and Waterfront Redevelopment in Chicago, Vancouver, and Toronto
Bibliographic record
Abstract
This article investigates the impact of public land ownership on long-term processes of urban development by comparing the political histories of waterfront redevelopment in Chicago, Vancouver, and Toronto. The study is driven by two research questions: Why have redevelopment efforts in Chicago and Vancouver apparently succeeded whereas those in Toronto failed? And what was the impact of public land ownership on these outcomes? Drawing from archival, interview, and geospatial data, I argue that land ownership conditions had a defining and enduring impact on the shape and scale of waterfront redevelopment in each city. What separates Toronto’s waterfront from Chicago and Vancouver is not how much land was historically controlled by public versus private owners, but rather the relative distribution and concentration of these assets. Early political events involving the consolidation or fragmentation of land ownership established institutional arrangements that either enabled or inhibited effective implementation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".