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Record W4288067938 · doi:10.1080/10511482.2022.2093939

Land-Use Planning Approaches to Near-Campus Neighborhoods and Student Housing Development Patterns in Ontario, Canada

2022· article· en· W4288067938 on OpenAlexaffabout
Nick Revington, Alexander Wray

Bibliographic record

VenueHousing Policy Debate · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsWestern UniversityInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsRedevelopmentContext (archaeology)Urban planningVariety (cybernetics)Environmental planningRentingIntervention (counseling)Public housingBuilt environmentAccommodationLand useLand-use planningEconomic growthGeographyBusinessPolitical scienceCivil engineeringPsychologyEngineeringEconomics

Abstract

fetched live from OpenAlex

Student housing represents a contentious local policy issue in university cities in North America and beyond, related to both the conversion of existing housing to student rentals and the development of private purpose-built student accommodations. Yet little research has considered the variety of approaches municipalities across an urban system have adopted to address student housing issues through land-use planning and their subsequent influence on development patterns. Through an analysis of planning documents in all 15 Ontario urban areas with a primary campus of the province’s 20 public universities, we identify four broad approaches. These range from minimal intervention to covertly restricting student housing development, directing student housing away from established neighborhoods, and enabling high-density redevelopment of a residential district to accommodate students. Municipalities’ approaches reflect local context, notably their relationship to the province’s regional growth management plans. Case studies demonstrate how each approach has shaped purpose-built student accommodation development locally.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.061
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.002
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.105
GPT teacher head0.310
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 source (direct Gemma or distilled Codex), 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

Citations13
Published2022
Admission routes2
Has abstractyes

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