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Record W4382365335 · doi:10.7202/1100576ar

Liens sociaux, initiatives collectives et processus conflictuels : le cas des mobilisations contre la gentrification à Parc-Extension, Montréal

2023· article· fr· W4382365335 on OpenAlexaffvenueabout
Emanuel Guay, Alessandro Giuseppe Drago

Bibliographic record

VenueRecherches sociographiques · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesSociologyArt

Abstract

fetched live from OpenAlex

Notre article vise à examiner les rapports entre les liens sociaux, les initiatives collectives et les processus conflictuels à partir de deux axes principaux. Nous prêtons d’abord attention à la dimension relationnelle des initiatives collectives, en nous penchant sur trois concepts qui peuvent nous aider à mieux saisir les processus menant à la formation, la reproduction et la transformation des liens sociaux. Nous prenons ensuite en compte le rôle que les initiatives collectives peuvent jouer dans le cadre de conflits sociaux, afin de complémenter l’accent mis sur la coopération dans plusieurs travaux portant sur ces initiatives. Nous croisons ces deux axes en analysant les mobilisations contre la gentrification observées entre 2019 et 2022 dans le quartier montréalais de Parc-Extension, à partir d’une recherche ethnographique menée durant cette période avec le Comité d’action de Parc-Extension (CAPE). Notre analyse nous amène à soutenir que les initiatives collectives peuvent à la fois contribuer à la reproduction sociale dans un environnement donné, répondre à des besoins et à des aspirations collectives et augmenter la capacité d’intervention populaire dans le cadre de processus conflictuels qui visent, entre autres, à déstabiliser les élites économiques et politiques et à obtenir des concessions.

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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.655

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0230.014
Scholarly communication0.0080.003
Open science0.0020.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.224
GPT teacher head0.389
Teacher spread0.165 · 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
Published2023
Admission routes3
Has abstractyes

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