Referral Criteria for Special Education: General Education Teachers’ Perspectives in Canada and the United States of America
Bibliographic record
Abstract
General education classroom teachers initiate the referral process of students who later become identified with a disability. With recent changes in educational assessment and programming (i.e., Response to Intervention [RTI] within the reauthorization of the Individuals with Disabilities Education Improvement Act [IDEIA] [2004] and Canadian legislation, such as Ontario's Education Quality and Accountability [EQAO] Office Act [1996]), this study examined the psychometric properties of referral criteria used by teachers when referring students for special education services. The sample (N=97) consisted of Canadian and American general education classroom teachers from rural, suburban, and inner-city school districts. Exploratory factor analysis identified that the 15 referral criteria can be reduced into two subscales, inattention and aptitude, and, collectively, they accounted for 50% of the common variance in teachers’ ratings of referral criteria. Suggestions for future research are also discussed.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".