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Record W4300032369 · doi:10.51952/9781847422446.ch004

Canadian housing allowances

2007· book-chapter· en· W4300032369 on OpenAlexaboutno aff
Marion Steele

Bibliographic record

VenuePolicy Press eBooks · 2007
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Housing allowances in Canada are offered by only four provinces. The absence of a national allowance should not be too surprising in a country where there is really no such thing as national policy for low-income housing. Instead there is a set of housing policies, one for each province. This has been especially true since the federal government all but vacated this area in the 1990s. It first terminated all programmes for building new social housing, as low- and mixed-income housing is called in Canada. Then it transferred the management of most existing subsidy commitments to the provinces. Even in the 1960s to the early 1980s when the federal government took an activist role, low-income housing in any province was only built if a province accepted the federal offer of funds and joined as a partner.1 This is in line with the fact that housing is constitutionally the responsibility of the provinces – a quite different situation from that of the US states relative to their federal government – and Quebec, especially, has been sensitive to this.2 The Canadian provinces have differed in their take-up of federal offers. In part this is because they have widely varying housing markets. For example, some like Ontario, Alberta and British Columbia (BC) have high-rent and high-cost cities and others such as Newfoundland, Quebec and Manitoba have not. They also have varying views of their own needs and the role of governments, with, for example, Alberta generally favouring a highly restricted role, Quebec an interventionist one and other provinces, such as Ontario, a more mixed role.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.084
GPT teacher head0.257
Teacher spread0.172 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

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