Géographie des seringues à la traîne : analyse du quartier montréalais Centre-Sud
Bibliographic record
Abstract
Ce mémoire s’intéresse à la distribution spatiale des seringues dans le quartier Centre-Sud de Montréal. De nombreuses études portent sur les seringues à la traîne depuis les années 1980, mais elles s’intéressent surtout aux consommateurs, à savoir s’ils utilisent ou non les divers programmes mis à leur disposition pour ramasser les seringues souillées. La grande majorité des études sont donc qualitatives et ne s’intéressent pas à la géographie des seringues à la traîne. L’objectif principal de cette étude est de décrire la distribution spatiale des seringues et de la mettre en relation avec la localisation des boîtes de dépôt public entre 2010 et 2014. Pour se faire, plusieurs méthodes d’analyses ont été mobilisées, dont le NKDE (Network Kernel density estimation) et une version locale de l’indice de Moran (LISA), le ILINCS. Par la suite, une modélisation a été réalisée à l’aide de plusieurs variables, dont la proximité aux boîtes de dépôt public, à divers organisations s’occupant de la gestion des seringues souillées ainsi que les différents types de rue. Les résultats nous permettent de constater que les seringues ne sont pas distribuées uniformément dans le quartier Centre-Sud. En effet, certains secteurs sont composés uniquement de points froids et les points chauds se retrouvent majoritairement entre les rues Sanguinet et Montcalm, ainsi qu’entre la rue Sherbrooke et le Square Viger. Concernant les résultats des régressions Tobit, les variables les plus significatives sont les ruelles et la proximité aux boîtes de dépôt public. This master thesis addresses the spatiality of discarded needles and syringes in Centre-Sud, a neighbourhood adjacent to downtown Montréal. Since the 1980s, the scientific literature enhances its focus mostly on attendance regarding needle exchanges programs for injection drug users and less on its spatial distribution within urban public space. Therefore, the objective of the present study is to describe the geography of discarded needles and then to explore its relationship with drop boxes between 2010 and 2014. We used a network kernel density estimation (NKDE), a local version of the Moran Index (LISA) (ILINCS) to achieve our first objective. Then, we modelized what we think could explain these concentrations. Variables such as the presence drop boxes and the networks of streets and alleys were used. Our results indicate that discarded needles are not uniformly distributed in the Centre-Sud neighbourhood. In fact, some sectors had only coldspots. Most of the hotspots were around Sanguinet, Montcalm, and Sherbrooke streets. Square Viger, a urban park located next to a highway, showed also a high concentration of used needles. The strongest variables explaining these hotspots for the study period were the presence of alleys and drop boxes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".