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Record W2945730225 · doi:10.1111/capa.12318

The privatization of housing assistance: Are housing allowances eroding government accountability?

2019· article· en· W2945730225 on OpenAlexaboutno aff
Maroine Bendaoud

Bibliographic record

VenueCanadian Public Administration · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityVoucherSubsidyPublic housingGovernment (linguistics)Subsidized housingPublic administrationRental housingPrivate sectorOptimal distinctiveness theoryEconomicsBusinessEconomic growthRentingPolitical scienceMarket economyLawAccounting

Abstract

fetched live from OpenAlex

Abstract A major change in the low‐income housing sector across nations has been the introduction of housing allowances (or vouchers/benefits) typically used on private rental markets. This change is a fundamental shift from post‐war housing policy when government was a main provider through publicly owned housing. This “privatization” is usually associated with the New Public Management. This article addresses this policy change and discusses the implications for the accountability of governments in Quebec, British Columbia, and Alberta. This article argues that the policy shift from in‐kind to in‐cash has not eroded accountability due to the distinctiveness of housing assistance, compared to other social programs. Actors involved in the housing community continue to challenge governments with regard to consumption subsidies, emphasizing their flaws and imperfections. The argument is empirically probed through a comparative analysis over 50 years, drawing on government archival records and housing advocates’ publications.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.844
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.220
Teacher spread0.193 · 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 teacher head, 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

Citations6
Published2019
Admission routes1
Has abstractyes

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