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Record W3208534101 · doi:10.32920/ryerson.14646387.v1

Employment lands in Toronto: a case for conversion in urban centres and in the vicinity of transit stations

2021· preprint· en· W3208534101 on OpenAlexaffabout
Sean Guenther

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDeindustrializationSmart growthLand useGeographyPopulationUrban planningEnvironmental planningLand-use planningBusinessCity centreEconomic growthAgricultural economicsCivil engineeringEngineeringEconomicsArchaeologySociology

Abstract

fetched live from OpenAlex

Deindustrialization and rapid population growth in the City of Toronto has resulted in greater employment land conversion pressures being placed on underutilized and vacant Employment Areas (Blais, 2015; Filon 2003). In 2013, City Planning Staff made recommendations to City Council for the preservation or conversion of specific employment land application requests under the City of Toronto’s Municipal Comprehensive Review process (City of Toronto, 2013). This paper will examine five employment land conversion applications in Toronto’s inner suburbs, the Scarborough Urban Growth Centre and within 500 meters of the Mimico GO Station through a content analysis of City Planning Staff’s recommendations along with the property owner’s rationales. It was found that the five sites should be converted to better meet the Provincial and Municipal planning policy requirements that align with Smart Growth’s objectives. The five sites pose minimal land use compatibility conflicts, require increases in population and employment density, and are isolated from larger Employment Areas.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.037
GPT teacher head0.335
Teacher spread0.299 · 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 designQualitative
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 routes2
Has abstractyes

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