Did Segregation Increase as the City Expanded?
Bibliographic record
Abstract
Montreal in 1881 was highly segregated along four distinct social dimensions: language, religion, socioeconomic status, and sector of employment. By 1901 the population had doubled, and we examine changes in residential distributions over the two decades. Despite the increased integration of certain groups, segregation remains high, and multiple dimensions are still discernible. In addition to long-established communities of French Canadians, Irish Catholics, and Anglo-Protestants, we see new streams of immigrants occupying their own patches in the urban fabric. To make meaningful observations of sociospatial changes over two decades, we used a geographic information system (GIS) to situate individual census households with spatial precision on 1 of 12,000 lots in 1881 and 30,000 in 1901, so that we could reaggregate them into meaningful districts of different scales and districts with identical boundaries for both years of observation, thereby overcoming the major methodological problems hindering previous comparative analyses. Coupling well-established statistical indexes of segregation and diversity in a GIS framework lends new analytic power to grasp the scale of phenomena and inquire into behavioral choices of nineteenth-century households. The empirical evidence shows how both concentration and diversity were built into the urban fabric. This study also offers methodological cues for comparative studies in other places and periods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".