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Record W2548213539 · doi:10.1177/1078087416671429

Failure When Fragmented: Public Land Ownership and Waterfront Redevelopment in Chicago, Vancouver, and Toronto

2016· article· en· W2548213539 on OpenAlexafffundabout
Gabriel Eidelman

Bibliographic record

VenueUrban Affairs Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRedevelopmentLand tenurePoliticsPublic administrationPublic landGentrificationConsolidation (business)Public housingPolitical scienceEconomic growthBusinessGeographyEconomicsLawFinanceArchaeology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.654
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.246
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations29
Published2016
Admission routes3
Has abstractyes

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