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Record W2942219451

Excluded from inclusion: Building social housing in times of austerity and “social diversity” (Quebec/France)

2017· article· en· W2942219451 on OpenAlexaboutno aff
Fabien Desage

Bibliographic record

VenueLillOA (Université de Lille (University Of Lille)) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAusterityPublic housingGovernment (linguistics)IncentiveInclusion (mineral)Metropolitan areaDiversity (politics)Political scienceEconomic growthPublic administrationSociologyGeographyEconomicsSocial science
DOInot available

Abstract

fetched live from OpenAlex

Depuis une dizaine d’années, en France comme au Québec, la fixation de taux minimum de logements sociaux est devenue l’un des outils privilégiés des pouvoirs publics pour favoriser le développement du parc social. Ces taux sont contraignants en France et incitatifs au Québec mais procèdent de logiques semblables, valorisant la mixité sociale comme objectif d’action publique et insistant sur les « opportunités de développement » que fourniraient les opérations privées, dans un contexte de baisse des financements publics. À partir de terrains réalisés dans deux agglomérations françaises (Nantes et Lille) et une agglomération québécoise (Montréal), il apparaît que l’acceptation du principe d’un « taux minimum de logements sociaux » n’a été concédée par les maires des communes résidentielles et par les promoteurs immobiliers, traditionnels opposants, qu’à la condition implicite d’en restreindre l’accès aux habitants issus de la commune (France) ou aux demandeurs sélectionnés par le réseau coopératif (Québec) ; autrement dit, d’en exclure les populations les plus indésirables et stigmatisées.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.991

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 studies0.0100.002
Scholarly communication0.0000.001
Open science0.0010.005
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.248
Teacher spread0.227 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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