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Record W4237656784 · doi:10.32920/ryerson.14658075

Analysis of vacant and abandoned properties in the City of Toronto

2021· preprint· en· W4237656784 on OpenAlexaffabout
Vanessa Fletcher

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
Fundersnot available
KeywordsAbandonment (legal)Stock (firearms)BusinessInvestment (military)GeographyPolitical scienceArchaeologyPolitics

Abstract

fetched live from OpenAlex

The city of Toronto is in the midst of unprecedented development fuelled by low-interest mortgage rates, foreign investment and staggering commute times. Despite this, a small percentage of properties remain vacant or abandoned in Toronto. This paper identifies the types of properties that are vacant and abandoned, assesses the property stock for degree of decay and re-examines the appropriateness of the classification of 'vacant' and 'abandoned'. This research indicates that vacant buildings in Toronto transition to abandonment for reasons that may be different from American cities. Vacancy in Toronto may be more affected by lengthy planning approvals, development financing and issues of site acquisition, more than urban decay or blight. The research further indicates that most vacant and abandoned buildings in Toronto are residential, commercial vacancy is transient by nature and industrial abandonment is found mostly along existing railway corridors.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.805

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 head0.062
GPT teacher head0.307
Teacher spread0.246 · 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 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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