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Record W2527470978 · doi:10.4000/gc.11419

Les mégaplexes cinématographiques et le nouvel environnement périurbain de la région métropolitaine de Montréal

2005· article· fr· W2527470978 on OpenAlexaffabout
Gilles Sénécal

Bibliographic record

VenueGéographie et cultures · 2005
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsSociologie et SociétésInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Les mégaplexes accaparent actuellement près de 60 % de l’offre cinématographique montréalaise, en prenant comme indicateur le nombre de places assises. Des grands théâtres du centre-ville transformés en salle de cinéma à la fin du XIXe siècle jusqu’aux mégaplexes d’aujourd’hui, les salles de cinéma prennent différentes formes et leur localisation dans l’espace métropolitain rend compte de l’évolution de la structure urbaine. La présence des mégaplexes témoigne également de la consolidation et de la diversification des espaces périurbains, puisqu’ils logent à proximité de pôles d’emploi et de lieux de services et d’activités. L’observation de telles variations de l’offre cinématographique permet d’engager une réflexion sur les styles de vie. Les mégaplexes de banlieue s’inscrivent dans un espace aménagé en faveur de l’utilisation de l’automobile personnelle et ils participent ainsi à la constitution des trajectoires formant l’espace vécu des résidents des espaces résidentiels périurbains.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.111
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

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 head0.018
GPT teacher head0.295
Teacher spread0.278 · 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
Published2005
Admission routes2
Has abstractyes

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