School Choice and the Polarization of Public Schools in A Global City: A Bourdieusian GIS Approach
Bibliographic record
Abstract
Over the past three decades, urban sociologists have shed light on the intensifying social inequality between the wealthiest and poorest neighborhoods in global cities; yet limited research has been done to illuminate the relationships between urban polarization and school choice (i.e., where parents choose schools for their children). This study sociospatially examines the patterns of secondary school choice in the global city of Toronto to illuminate the relationship between urban polarization and school choice. In doing so, this study combines Pierre Bourdieu’s sociospatial theory with a geographic information systems (GIS) approach. Overall, we found that popular schools and schools with specialized choice programs tend to be located in high-status neighborhoods, defined as neighborhoods with residents in the top 20% of family income, home prices, education attainment, and representation from the dominant culture. We also show that mobile students who choose popular schools or highly sought-after specialized programs tend to come from advantaged neighborhoods. Meanwhile, local students who choose a regular school in their neighborhood tend to come from low-status neighborhoods. With a new interdisciplinary approach, this study contributes to a more spatialized understanding of how social inequality and polarization account for school choice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".