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Record W4293064630 · doi:10.32920/19067726

Employment land conversion and intensification corridors: exploring the role of municipal, regional and provincial policy in York Region

2022· preprint· en· W4293064630 on OpenAlexaffabout
Andrew Haagsma

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLegislationRegional policyPlan (archaeology)Industrial policyEnvironmental planningLand useRegional planningBusinessRegional scienceUrban planningGeographyPolitical scienceEngineeringCivil engineeringInternational trade

Abstract

fetched live from OpenAlex

<div>There seems to be an overall consensus in literature that industrial lands are not to be overlooked in the wake of conversion pressures to accommodate additional housing and other types of development. It is evident in literature that smart growth policies have tended to disregard the revitalization of industrial activities and this is problematic because a vital industrial presence is important in the dynamic of commercial and residential intensification.</div><div>This paper explores current legislation and policy applicable to employment lands located along intensification corridors in York Region, Ontario, Canada. This is completed with three different components: a literature and policy review; a review of regional comments on conversion requests; and a site-specific case study analysis of applicant justifications for conversions in intensification areas in York Region. Research findings include a robust industrial land intensification initiative in the Region, market demand along Regional Intensification Corridors, and the potential for the implementation of an area-specific plan in the Keele employment area in the City of Vaughan. Recommendations include policy directions and considerations for planning professionals.</div>

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.943

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 head0.099
GPT teacher head0.303
Teacher spread0.204 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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