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Record W4248830535 · doi:10.32920/ryerson.14653740

Vertical expansions : assessing the feasibility of building private residential space atop existing public community centres and libraries through a public-private partnership in Toronto's inner-suburbs

2021· preprint· en· W4248830535 on OpenAlexaboutno aff
Pirijan Ketheswaran

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipAppealPublic–private partnershipGovernment (linguistics)BusinessSpace (punctuation)Public administrationEngineeringPolitical scienceFinanceComputer science

Abstract

fetched live from OpenAlex

The aim of this study is to analyze the feasibility of building private residential space on top of existing publicly-owned inner-suburban Toronto community centres and library buildings through a public-private partnership. The numerous benefits and feasibility of these 'vertical expansions' to the community and government is validated by the potential ability to increase local access to services, compatibility with wider government planning objectives such as smart growth, and by the capacity to kickstart a virtuous economic cycle to increase quality of place. The appeal to private developers is demonstrated through considerations of the market, money, production, people and environment. Pilot sites where vertical expansions could best succeed are identified based on facility type, 'city 3' suburban status, potential marketability, and basic physical & structural considerations. 16 pilot sites were identified, which were further refined based on property value to identify the pilot sites with the highest potential for success. A model for a reductionist and mutually equitable P3 arrangement for both the development and post-construction management was proposed and informed by the literature review of past P3 case studies.

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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.004
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 head0.240
GPT teacher head0.402
Teacher spread0.162 · 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 designTheoretical or conceptual
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

Citations0
Published2021
Admission routes1
Has abstractyes

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