Educational Currency: Teacher-Parents, Privilege, and Schools Choice in Ontario
Bibliographic record
Abstract
Neoliberal school choice policies continue to expand across the globe. Supporters claim that the school system’s failings can be solved by moving away from a one-size-fits-all, geographically allocated body, and towards a more diversified, individualized market. At the forefront of this movement are choice advocates who insist that it is more equitable to provide families with the opportunity to decide where their children will learn and that school markets are more socially responsive. However, these sentiments are not undisputed. The following study is built on the opposing position – that current school choice policies are discriminatory and unjust. I challenge the notion that markets enhance equity by investigating the complex process of how schools are selected. Because neoliberal, market methods are rooted in competition, I emphasize that the practice of school choice is yet another opportunity in which families experience uneven amounts of advantage. But, while many agree that school choice policies privilege certain parents more than others, little is known about what this privilege looks like, how it is attained, and how it is used during the selection process. In this study, I attempt to understand the intricacy of school market privilege by studying teacher-parents, a subgroup of the population who possess it. The experiences of teacher-parents provided insight into how privilege may be differently retained, acquired, and used in school choice. This is the story of how twenty-three public school teachers navigate the marketed school system in Ontario, Canada. In this study, participants share their insider experience and demonstrate how their unique combinations of cultural, social, and economic capital allowed them to collect and spend (use) educational currency (EC) as they choose schools for their own children. The data not only reaffirms that certain populations possess unique amounts of educational currency and provides insight into what exactly EC is; it also unveils that choice can have a concerning tendency to shape a more racially, ethnically, and economically segregated system. This information has become even more relevant since the as the Covid-19 global pandemic has created novel concerns and options including various forms of online and distance learning. Word count: 348. Keywords: school choice, privilege, educational currency
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".