L’autocorrélation spatiale et les données de santé : une étude préliminaire
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".