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Record W2903202352 · doi:10.1080/13563475.2018.1552565

The urban dormitory: planning, studentification, and the construction of an off-campus student housing market

2018· article· en· W2903202352 on OpenAlexafffundabout
Nick Revington, Markus Moos, Jeff Henry, Ritee Haider

Bibliographic record

VenueInternational Planning Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsUrban planningRealmStructuringNeighbourhood (mathematics)Unit (ring theory)Investment (military)BusinessEconomic growthEnvironmental planningPolitical scienceCivil engineeringEngineeringGeographyFinanceEconomicsPolitics

Abstract

fetched live from OpenAlex

Regulating the negative impacts of private off-campus student housing on neighbourhoods, especially where it is concentrated by processes of ‘studentification,’ is a pressing planning issue in the knowledge economy city where universities are expanding. We piece together a history of planning for student housing in Waterloo, Ontario from 1986 to 2016 through an analysis of planning documents. Over this time, planning has proactively anticipated changes and attempted to direct development accordingly in ways that extend beyond ‘studentified’ areas. We therefore argue for greater attention to the broader ‘urban dormitory’ in which students live across the city. Lessons from Waterloo illustrate that planning in cities with significant off-campus housing must be adaptive to effectively manage the urban dormitory, as investment in high-density housing has alleviated supply constraints but did not prevent neighbourhood disruptions. A valuable role for planning is in structuring the public realm, providing amenities, and regulating unit size and design of new development.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.729

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.010
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.033
GPT teacher head0.399
Teacher spread0.366 · 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

Citations60
Published2018
Admission routes3
Has abstractyes

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