IT WAS WRITTEN ALL OVER HIM: CLASSROOM TEACHERS' REFERRAL CRITERIA FOR SPECIAL EDUCATION SERVICES
Bibliographic record
Abstract
A contextual understanding of general education classroom teachers’ reasons for a student’s referral for special education services provides insight into this initial step in the identification process. The philosophy of social constructivism (Bruner, 1987; Freedman & Combs, 1996; Vygotsky, 1978) provides a backdrop for the underlying practices and beliefs which render the participants in this study to employ the referral criteria that they use. Thirteen general education elementary teachers in a suburban city in southern Ontario were interviewed about their referral criteria for special education services. The results of this study indicated a combination of student characteristics that teachers observed (inattention, lack of comprehension, inability to complete tasks in the allotted time, and poor test performance) and what teachers inferred (e.g., about the way a student looks). The implications of the research for classroom and special education practices in particular are 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".