Special Education Representation Trends Vary by Language Status: Evidence of Underrepresentation in Tennessee
Why this work is in the frame
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Bibliographic record
Abstract
Using U.S. state-level data, we report unadjusted and adjusted odds ratio of special education (SPED) trends in Tennessee from 2009 to 2019 for students in Grades 3 to 8 by three language groups: native English speakers (NES), English-proficient bilinguals (EPB), and Current English learners (Current EL). We report trends across all SPED disability categories and across five prevalent disability categories (specific learning disability, specific language impairment, intellectual disability, other health impairments, and autism). The cross-sectional analytic sample included 812,783 students from 28 districts that met the SPED risk ratio threshold set by the state. Results revealed that, compared with NES students, both EPB and Current EL students were generally less likely to receive SPED services, suggesting evidence of language status disparities in SPED representation. Furthermore, findings varied depending on whether adjustments were made to generate odds ratios, especially for higher-incidence disabilities (specific learning disability, specific language impairment, and intellectual disability). Finally, the most severe evidence of underrepresentation was in lower-incidence disabilities (other health impairments and autism). Our results underscore the need for further examination into low rates of SPED identification among learners whose first language is not English (EPB and Current EL). We discuss the contextualized research, practice, and policy implications of our findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it