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Record W4312408059 · doi:10.5663/aps.v2il.17705

Housing and Aboriginal People in Urban Centres: A Quantitative Evaluation

2012· article· en· W4312408059 on OpenAlexaffvenueabout
Yale D. Belanger, Gabrielle Weasel Head, Olu Awosoga

Bibliographic record

Venueaboriginal policy studies · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsOvercrowdingMainstreamRentingHousing tenureEconomic growthBusinessState (computer science)SubdivisionGeographyPolitical scienceDemographic economicsEconomics

Abstract

fetched live from OpenAlex

This paper explores the current state of urban Aboriginal housing in Canada, by providing an up-to-date mapping of national urban Aboriginal housing conditions. This paper demonstrates that home ownership helps to reduce the gap between mainstream and Aboriginal rates of core housing need, for Aboriginal renters are substantially worse off than their non-Aboriginal counterparts in terms of core housing need and overcrowding. Métis and Non-Status Indians are also more likely to become homeowners than Status Indians and Inuit. A cyclical process is identified that hinders urban Aboriginal homeownership, and home rental advancement is also discussed. Existing federal housing programs are inadequate to address the housing and homeless issues identified. We highlight the need to establish proactive policies, the goal being to facilitate individual transition into urban centres, thereby helping to ameliorate existing housing disparities.

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)0.011
metaresearch head score (Gemma)0.018
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 score0.662
Threshold uncertainty score0.671

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.062
GPT teacher head0.359
Teacher spread0.297 · 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

Citations4
Published2012
Admission routes3
Has abstractyes

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