When Special Education Policy in Ontario Creates Unintended Consequences
Bibliographic record
Abstract
This chapter examines the unintended consequences of the Canadian province of Ontario’s policy requirement that students receive formal assessments before they are entitled to access special needs identification and services. While Ontario legislation states that all students with special needs should receive the supports and services they require for success in the province’s public schools, the assessment policy has unintentionally perpetuated inequities between students from different classes and regions. These unintended consequences are due to the fact that some students encounter long waiting lists to receive required assessments. We analyze this policy in conjunction with an important aspect of its context: the advocacy of People for Education (P4E), a prominent non-governmental organization in Ontario that brought the policy’s unintended consequences to light and has pushed for policy change. The chapter draws on findings from a study that used rhetorical analysis to identify P4E’s efforts to change practices surrounding special education assessment from 1996 to 2016. Findings from the analysis were examined in relation to the broader contexts within which Ontario’s assessment policy is situated to understand how these contexts have affected both the policy and P4E’s as-yet-unsuccessful advocacy to change the policy and address wait times for special education assessments. These broader contexts include funding shortfalls, increasing privatization in education, and neoliberal conceptions of good parenting. We conclude by suggesting a number of possible changes that could address the inequities perpetuated by the policy.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.029 | 0.020 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".