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Record W4416926324 · doi:10.36939/cjur/vol31no1/art388

Housing policies and Montreal’s neighbourhoods: Social mix or social exclusion?

2022· article· W4416926324 on OpenAlexaffvenueabout
Hélène Bélanger, Renaud Goyer

Bibliographic record

VenueCanadian journal of urban research · 2022
Typearticle
Language
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsDisadvantagedPublic housingNeighbourhood (mathematics)PoliticsZoningSocial policyCriticismPublic policyTechnocracy

Abstract

fetched live from OpenAlex

Different housing initiatives, policies and programs favouring or impacting social mix at the neighbourhood level exist in Canadian cities, including in the city of Montreal. Social mix was (and still is) part of the political discourse along with local planning practices as a means of including the most deprived and marginalized populations within the urban space through social housing. With the aim of developing a more inclusive city, the local administration has recently adopted an inclusionary zoning by-law which was received positively by scholars and housing advocates, although the considerable criticism for its limited scope. Inspired by a constructivist analysis of public and social policies and based on a study of collective identity of housing groups using document analysis, the goal of this paper is to explore how urban social movements evaluate this housing policy, and how, despite the policy objective of inclusiveness, it cannot satisfactorily address core housing needs of disadvantaged populations.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.055
Threshold uncertainty score0.397

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.008
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0010.001
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 head0.101
GPT teacher head0.370
Teacher spread0.269 · 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 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
Published2022
Admission routes3
Has abstractyes

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