Special Education Pre-Referrals in One Public School Serving Native American Students
Bibliographic record
Abstract
When teachers lack cultural competence, any gap between students' home life and school life can disadvantage learners. In this case study of a school where one-quarter of the student population is Native American, I examine how two White general-education teachers decided to refer students to the special education pre-referral team. All referred students were Native American.1 Analysis of interviews, observations, and documents revealed several themes: (a) the use of antiquated frameworks to make referral decisions, (b) dissonance between participants' perceptions and actions, and (c) complicated understandings of culture's influence on referrals. Participants claimed to see all students the same while believing that Native American and White students learn differently. Lack of multicultural competence generates these contradictory notions, which can result in overrepresentation of minority students in special education. I conclude with implications for teacher-preparation programs.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".