Modeling the geospatial dynamics of residential segregation in three Canadian cities: An agent‐based approach
Bibliographic record
Abstract
Abstract Long‐term residential segregation can exacerbate social inequality and exclusion in urban populations. Existing models of segregation aim to represent and better understand drivers of segregation and assess possible segregation effects in response to incoming immigrant populations. However, these studies are not typically implemented on real geospatial data to represent the urban environment, and even less frequently compare patterns of segregation between cities. Therefore, the objective of this study is to implement an agent‐based model that simulates the decision‐making process of immigrants as they arrive and settle in three Canadian gateways for immigration, including the City of Toronto, the City of Calgary, and Metro Vancouver. The resulting simulated spatial patterns of segregation are visually compared to real data representing the location of hotspots of immigrants of various ethnic origins. The degree of segregation is measured and compared, with measures of segregation obtained from actual census data. The spatial patterns and degree of segregation are compared across the three study areas. The developed model has the potential to be used as a tool for knowledge discovery and decision‐making in the processes of city planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".