Special education eligibility trends and related factors: An analysis of the context in Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract While there has been movement from disability, or special education needs (SEN), eligibility identification as a prerequisite for special education services, formal school‐based identification remains critical in understanding the educational experience of students with disabilities. This study examined data from Ontario, Canada between 2006 and 2020. Descriptive statistics described trends in SEN status over the 14 years across 13 SEN categories. In addition, variance in the proportion of students identified per enrolled each year was examined to determine whether significant relationships existed with school level (i.e., elementary or secondary), school board type (i.e., English public, English Catholic, French public and French Catholic) and school board size. Analysis revealed an increase in special education eligibility determinations with statistically significant increase in autism and non‐identified eligibility and a decrease in mild intellectual disability. Hierarchical regression analysis revealed that school level, school board type and school board size were also significant predictors of identification. The multidirectional effects of each variable and their implications are explored.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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 it