Identifying and Measuring Dimensions of Urban Deprivation in Montreal: An Analysis of the 1996 Census Data
Bibliographic record
Abstract
This paper uses data from the 1996 Canadian census to examine and measure the spatial structure and intensity of urban deprivation in Montreal. Urban deprivation emerged as an important theme in urban studies and urban geography during the 1990s. Since the early 1980s, the Montreal urban area, particularly the Island of Montreal, has experienced an increase in urban social problems, brought on largely by economic restructuring, recessions and the out-migration of residents and businesses to suburban communities. Twenty indicators of urban deprivation are drawn from the census and analysed by way of a principal components analysis first to identify the main types of deprivation in the city and then to measure its intensity. In the process, a general deprivation index (GDI) is devised which can be applied to study the spatial aspects of this phenomenon in other Canadian cities. The study identified six main types of deprivation in the city and found that they were most visible on the Island of Montreal, especially in the central and eastern parts. Additionally, it found that urban deprivation in not confined to the inner city, as several of the most severely deprived neighbourhoods are located outside the central city and even in the off-Island suburbs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".