MétaCan
Menu
Back to cohort
Record W3184512608 · doi:10.1111/cag.12706

Public housing, market rentals, and neighbourhood characteristics

2021· article· en· W3184512608 on OpenAlexafffundvenue
Catherine Leviten‐Reid, Melanie MacDonald, Rebecca A. Matthew

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsCape Breton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNeighbourhood (mathematics)RentingPublic housingPovertyDisadvantageRental housingLow incomeBusinessAffordable housingEconomic growthDemographic economicsPublic economicsEconomicsPolitical science

Abstract

fetched live from OpenAlex

Market‐based rentals are increasingly prioritized as the tenure type through which to provide housing assistance to low‐income households: it is argued that public housing places tenants in neighbourhoods with concentrated poverty, while the private sector is purported to offer households the opportunity to live in locations with less disadvantage. We test this assumption through a case study of a Nova Scotian municipality. Using chi‐square tests to examine associations between housing type and neighbourhood deprivation, we find that while 47% of public units are located in places with high social and economic deprivation, one‐third of market rentals are located in neighbourhoods with similar characteristics. In addition, in looking at the location of lower‐cost market units in particular, we find limited differences in the neighbourhood characteristics in which these more affordable rentals and public housing are found. We argue for the importance of connecting policy and programming to place‐based community development and poverty‐reduction strategies to support low‐income households.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0030.003
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.221
Teacher spread0.199 · 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; both teacher heads agree on what is shown here.

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

Citations16
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Geographies / Géographies canadiennesSame topicUrban, Neighborhood, and Segregation StudiesFrench-language works237,207