Modélisation spatiale de la pauvretéà Montréal: apport méthodologique de la régression géographiquement pondérée
Bibliographic record
Abstract
Spatial Modeling of Poverty in Montréal: Methodological Contribution of the Geographically Weighted Regression The Island of Montréal is particularly concerned with the issue of poverty. In 2000, 29 percent of its inhabitants lived under the low income cut‐offs as defined by Statistics Canada. However, poverty is not a homogeneous phenomenon at the intra‐urban scale, and identifying and categorizing spaces of poverty has become a main concern for ongoing researches. According to this way of thinking, this paper proposes an analysis of the factors influencing the geographical distribution of poverty on the Island of Montréal. To be able to identify properly the various profiles of poverty, this analysis uses a specific methodology, the geographically weighted regression (GWR), and compares its results with the ones of a classical regression model. At the global level, the most important factors to explain poverty are in order: unemployment, lone‐parent families, one person households, recent immigrants, part time or part year workers, school dropouts. At the local level,
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".