Educational currency: The divisiveness of school choice policies in Ontario, Canada
Bibliographic record
Abstract
School choice policies continue to expand across the globe. Advocates insist that the opportunity to decide where one’s children will learn is more equitable and socially responsive. However, these sentiments are widely disputed. In this study I emphasize that school choice is another venue where families experience uneven amounts of privilege. While there is extensive literature documenting that unequal advantage exists in school markets, little is known about what this advantage looks like, how it is attained, and how it is used in Ontario, Canada. This research unveils the intricacy of educational currency by studying teacher-parents, a subgroup of the population who possess it. Educators in Ontario share how their unique combinations of cultural, social, and economic capital allow them to collect and spend educational currency (EC) as they choose schools for their own children. The data not only reaffirms that certain populations possess unique amounts of EC and defines what EC is; it provides insight into how school choice leads to a more racially, ethnically, and economically segregated system.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".