Unequal City and Inequitable Choice: The Neoliberal State’s Development of School Choice and Marketization in the Publicly Funded Catholic School Board in Toronto, Canada
Bibliographic record
Abstract
This study examines the extent to which school choice in the Toronto Catholic District School Board impacts equity and segregation. This examination is important because full public funding for the Board should adhere to the goals of public education, namely, equity and inclusion of all students. A critical policy geography perspective is applied to illuminate the dynamics of school choice as a neoliberal reform in the context of a global city where residential polarization and occupational bifurcation along racialized social class lines have intensified. Guided by critical space analysis, this research uses student enrollment data (Grades 9–12), Canadian Census data, school website information, and secondary literature. The findings suggest that school choice increases spatial inequity by giving those who are already socially and racially advantaged easier access to prestigious academic programs of choice. School segregation according to students’ economic backgrounds thereby increases. This study calls for implementing sociospatially conscious education policies that can undo rather than reinforce global city inequality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".