MétaCan
Menu
Back to cohort
Record W4382365308 · doi:10.7202/1100577ar

Communs culturels territoriaux et COVID-19 : le cas du quartier Saint-Michel à Montréal

2023· article· fr· W4382365308 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRecherches sociographiques · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicContemporary art, education, critique
Canadian institutionsUniversité TÉLUQUniversité du Québec à Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesSociologyArt

Abstract

fetched live from OpenAlex

Depuis le début des années 2000, la culture est devenue un élément important des stratégies de développement du quartier Saint-Michel. Ces stratégies ont fait émerger un mode de gouvernance des dynamiques culturelles associant acteurs culturels, politiques et communautaires. Coordonnés par une « table culture », des évènements de la vie culturelle du quartier sont ainsi produits et gérés en commun. La crise de la COVID-19 a fait apparaitre une arène d’action qui a infléchi les dynamiques culturelles du quartier. Elle a été une source d’innovations pour les acteurs michelois. La réponse culturelle communalisée aux problèmes induits par la pandémie a constitué une opportunité d’enrichissement du capital « socioterritorial » du quartier. La notion de commun culturel territorial (CCT) permet de mieux comprendre ce processus. Les CCT génèrent des dynamiques capables d’agir sur les dimensions matérielles et symboliques des environnements urbains pour concourir à la qualité et à la revitalisation des milieux de vie.

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.

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)0.013
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0020.002
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.123
GPT teacher head0.405
Teacher spread0.283 · 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