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Record W2765607627 · doi:10.7202/1041222ar

Géographie des seringues souillées à la traîne à Montréal

2017· article· fr· W2765607627 on OpenAlexaffvenueabout
Élaine Lesage‐Mann, Philippe Apparicio

Bibliographic record

VenueCahiers de géographie du Québec · 2017
Typearticle
Languagefr
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsHumanitiesGeographyPolitical scienceArt

Abstract

fetched live from OpenAlex

Plusieurs études s’intéressent aux seringues à la traîne, mais elles sont souvent de nature qualitative et portent surtout sur les comportements des utilisateurs de drogues injectables. Pourtant, il serait très pertinent de connaître la localisation des seringues souillées abandonnées afin d’optimiser les programmes de gestion de seringues. L’objectif de cet article est donc de tracer un portrait spatiotemporel des seringues à la traîne dans le quartier montréalais Centre-Sud, entre les années 2010 et 2014. Au plan méthodologique, deux méthodes d’analyse spatiale récentes sont mobilisées : le Network Kernel Density Estimation (NKDE) et le I Local Indicators of Network-Constrained Clusters (ILINCS) qui utilisent la distance réticulaire plutôt que la distance euclidienne. Nos résultats montrent qu’il existe plusieurs concentrations importantes de seringues dans le quartier et qu’elles bougent peu durant les cinq années. Ces résultats permettent aussi de constater que l’utilisation de ces nouvelles méthodes d’analyse spatiale (NKDE et ILINCS) est tout à fait appropriée dans le cadre d’événements se déroulant sur des axes routiers et que ces méthodes mériteraient d’être plus largement utilisées en géographie urbaine.

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 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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.046
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueCahiers de géographie du QuébecSame topicData-Driven Disease SurveillanceFrench-language works237,207