The (Over)representation of Minority Students in Special Education Programs: Teachers’ Perspectives
Bibliographic record
Abstract
This study explored factors that may be contributing to the overrepresentation of minority students in special education programs in Ontario. To carry out this research project, two teachers from the Greater Toronto Area were interviewed using a semi-structured model. Findings included teachers’ beliefs that parental involvement is crucial when it comes to students being referred, assessed and diagnosed into the special education program, and that the minority students being overrepresented are students living in poverty, students from monoparental homes and English Language Learner students. These findings suggest that some parents may choose not to attend referral-related meetings because they may not feel sufficiently well-informed to make suggestions or provide feedback. These findings also suggest that if overrepresentation of minorities is a persistent issue in special education, there may be a problem with the way students are being identified and assessed. Recommendations include that schools create a parent orientation session about the special education referral process. This orientation section would be made available for parents who will be attending IEP and IPRC meetings in order to provide them with the support needed prior to attending individual meetings.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".