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Record W1828803271

Rethinking Shelter-Cost-to-Income Ratios in Housing Allowances

2015· article· en· W1828803271 on OpenAlexaboutno aff
Lara Eleanor Croll

Bibliographic record

VenueSummit (Simon Fraser University) · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEconomicsLabour economicsPublic economics
DOInot available

Abstract

fetched live from OpenAlex

The Canadian definition of housing affordability depends on a ratio, which states that housing is affordable if it costs less than 30% of gross household income. This ratio is used to determine both eligibility and benefit levels in many Canadian affordable housing programs, including social housing and housing allowances. However, this ratio is not a methodologically sound or equitable way to define housing affordability. The result is that affordable housing programs underserve large families living in high-rent urban regions. This study searches for an alternative method to define eligibility and allocate benefits within provincial housing allowances. British Columbia’s Rental Assistance Program is used to illustrate the application of concepts and measures. Four eligibility and two benefit allocation methods are evaluated. It is recommended that provincial housing authorities adopt Housing Income Limits and the Transfer method to determine eligibility and allocate benefits respectively in housing allowances targeted at families.

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)0.025
metaresearch head score (Gemma)0.111
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.775

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.111
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0010.003
Scholarly communication0.0050.004
Open science0.0030.002
Research integrity0.0010.003
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 head0.042
GPT teacher head0.212
Teacher spread0.171 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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