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Record W1511997811 · doi:10.7202/1025647ar

Ville ou banlieue?

2014· article· fr· W1511997811 on OpenAlexaffvenueabout
Sandrine Jean

Bibliographic record

VenueRecherches sociographiques · 2014
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsHumanitiesSociologyPolitical scienceArt

Abstract

fetched live from OpenAlex

Bon an, mal an, environ 20 000 personnes quittent la Ville de Montréal pour s’établir en banlieue. Dans ce contexte de concurrence pour attirer les jeunes ménages, nous nous sommes penchés sur les choix résidentiels de familles de la classe moyenne en faveur de la ville centrale ou de la banlieue. Cinquante et une entrevues approfondies ont été menées en 2011-2012 dans deux quartiers de la région métropolitaine de Montréal, l’un représentant la banlieue, Vimont-Auteuil, l’autre la ville centrale, Ahuntsic. Au-delà du prix des logements, les choix résidentiels des familles sont liés aux représentations de la ville et de la banlieue, aux usages du quartier et du chez-soi, à leur mobilité quotidienne, leur identité, et en somme leurs modes de vie. Les images négatives de la vie de famille en ville véhiculées par les banlieusards de même que la vision stéréotypée de la banlieue dépeinte par les urbains donnent à penser que l’opposition ville/banlieue est loin d’être caduque, du moins dans les représentations que s’en font encore aujourd’hui les jeunes familles de la classe moyenne.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.684
Threshold uncertainty score0.636

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0100.005
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0370.003

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 head0.164
GPT teacher head0.347
Teacher spread0.183 · 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 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

Citations9
Published2014
Admission routes3
Has abstractyes

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