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Making room for everyone

2024· article· en· W4402658216 on OpenAlexafffundvenueabout
Alex Petric, Dawn C. Parker, Sean Geobey, Kirsten Wright

Bibliographic record

VenueCanadian Planning and Policy / Aménagement et politique au Canada · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBusinessComputer science

Abstract

fetched live from OpenAlex

Over the past decade, Waterloo Region has experienced significant population growth and a resulting wave of housing construction, but house prices and rents have continued to rise. To tackle the broader housing crisis affecting Ontario and Canada, calls for increased housing construction have grown, but advocates often emphasize quantities of homes needed without considering housing type or household composition of populations in need. We analyze publicly available data to compare temporal and spatial trends in housing construction and population growth across Waterloo Region. We also assess housing indicators across household types and various socio-economic indicators to highlight groups deserving greater attention in community planning efforts. Based on our findings, we advocate including equity-based analysis in future housing and land needs assessments. We further discuss the potential for “missing middle” housing to address a wider variety of household needs and propose options for municipalities looking to support greater housing diversity.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.568

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0150.005
Scholarly communication0.0080.007
Open science0.0020.013
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.1700.072

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 head0.055
GPT teacher head0.367
Teacher spread0.312 · 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 designNot applicable
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
Published2024
Admission routes4
Has abstractyes

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