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Record W4244430697 · doi:10.32920/ryerson.14644917.v1

Publicly-led proactive and privately-led reactive planning : a comparison of comprehensive planning and development approaches between East Bayfront and King-Liberty Village

2021· preprint· en· W4244430697 on OpenAlexaffabout
Joshua Hilburt

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsToronto Metropolitan UniversityMcGill University
Fundersnot available
KeywordsDowntownBrownfieldRedevelopmentUrban planningInvestment (military)BusinessSustainabilityStrategic planningPublic administrationPublic investmentTransportation planningEconomic growthEnvironmental planningPolitical scienceEconomicsGeographyEngineeringTransport engineeringCivil engineeringMarketingLaw

Abstract

fetched live from OpenAlex

This research examines two sites in downtown Toronto undergoing large-scale comprehensive brownfield redevelopment schemes. While city agencies helped to spur initial investment in King-Liberty Village, most changes have been privately-led while the planning department has attempted to incrementally guide development and the inclusion of specific public amenities. Waterfront Toronto's planning of East-Bayfront is seen as a strategic public investment and has undergone proactive policy-led planning. These differing frameworks have resulted in contrasting outcomes. While the planning and development framework of the latter has created the conditions for a neighbourhood well-serviced by transit and more robust parkland, affordable housing and sustainability goals, it has required enormous public investment. The process in King-Liberty Village is more indicative of the challenges facing other private redevelopments that often require greater public planning resources than are available to ensure Toronto's continued growth is sufficiently accommodated.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.272
Teacher spread0.206 · 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.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2021
Admission routes2
Has abstractyes

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