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Record W2068478134 · doi:10.1068/c09153

Decentralisation and Devolution in Canadian Social Housing Policy

2010· article· en· W2068478134 on OpenAlexaffabout
Roberto Leone, Barbara Wake Carroll

Bibliographic record

VenueEnvironment and Planning C Government and Policy · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsBrock UniversityWilfrid Laurier University
Fundersnot available
KeywordsDemiseFederalismDevolution (biology)Dominance (genetics)DecentralizationBaby boomPublic administrationBoomGovernment (linguistics)Political sciencePublic housingSocial policyNational PolicyEconomic growthPoliticsPolitical economyEconomicsSociologyLaw

Abstract

fetched live from OpenAlex

Beginning in 1945 Canada had a relatively successful housing policy meeting the needs of returning veterans, baby-boom parents, and, later, baby boomers themselves in addition to those less advantaged through a number of income-support housing programs. The reasons for this success are well documented and largely relied upon the dominance of the federal government in this policy space. This was achieved despite housing being constitutionally primarily a provincial responsibility due to a process known as ‘cooperative federalism’. This success ended in the mid-1990s and Canada has not had a national housing policy, nor successful provincial policies, since that time. Much of the demise of housing policy can be attributed to what at the time was considered to be a unique Canadian federal phenomenon called ‘province building’. We look at the institutional arrangements which made for a successful housing policy for nearly fifty years and the institutional failings which led to its demise. In particular, we analyse why the unique position of municipalities in Canada vis-à-vis other federal states made it more difficult to deal particularly with planning and social housing problems in Canada. The lessons of Canada are apt for other federal states trying to trade off regional and ethnic interests versus national priorities.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.181
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.212
Teacher spread0.197 · 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 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

Citations45
Published2010
Admission routes2
Has abstractyes

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