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Record W2109037587 · doi:10.7202/021333ar

L’autocorrélation spatiale et les données de santé : une étude préliminaire

2005· article· fr· W2109037587 on OpenAlex
Diana C. Bouchard

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

VenueCahiers de géographie du Québec · 2005
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicSpatial and Panel Data Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesGeographyPhysicsPhilosophy

Abstract

fetched live from OpenAlex

L'analyse de l'autocorrélation spatiale cherche à mesurer jusqu'à quel point la variation dans un ensemble de données réparties dans l'espace est due aux relations de contiguïté. Du point de vue mathématique, il existe deux façons d'aborder le problème : l'analyse de variance et le calcul d'un coefficient d'autocorrélation. Dans cette étude, une méthode du deuxième type est appliquée d'abord à un ensemble de carrelages d'essai possédant divers degrés d'autocorrélation spatiale et puis à la distribution spatiale de mortalité due aux maladies chroniques, à Montréal, en 1972. On conclut qu'elles révèlent une autocorrélation faible mais significative par rapport aux données de mortalité, et que d'autres facteurs suggérés dans la littérature récente de la géographie médicale pourraient bien avoir plus d'influence que la contiguïté spatiale elle-même.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.016
GPT teacher head0.211
Teacher spread0.195 · 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