Longitudinal Patterns of Special Education/Inclusive Classroom Placement of Children with Conduct Problems: Correlates and Risk of School Dropout
Bibliographic record
Abstract
Conduct problems are among the most common reasons of referral to special education services and placement in special classrooms. Students with conduct problems are at a high risk of school dropout. However, little is known about the association between placement in special classrooms and the risk of school dropout for students with conduct problems. We employed data from a longitudinal study of students with conduct problems who were receiving special education services in special or in inclusive classrooms at study entry ( N = 302). Five patterns of placement in special (vs. inclusive) classrooms were identified. Higher academic performance and receptive vocabulary, and lower externalizing problems reduce the odds of persistent placement in special classrooms. Students with a persistent or delayed placement had higher risk of school dropout in comparison to students with no placement history. Students in special classrooms at study entry did not have a greater risk of school dropout if they later transitioned to inclusive classrooms. Strengthening the academic performance and receptive vocabulary of students with conduct problems could prevent placement in special classrooms. Limiting persistent and delayed placement in special classrooms may decrease the risk of school dropout among students with conduct problems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".