Striving for Homeostasis: Balancing the Inclusion of Students with an Emotional/Behavioral Disorder
Bibliographic record
Abstract
Social justice advocates have championed inclusive education, leading to its adoption in many jurisdictions. Despite policy changes designed to support learners with disabilities and research on inclusive education, students with an emotional/behavioral disorder (EBD) experience significantly poorer outcomes than their peers. Teachers often describe including this group of students as impossible and attempts to achieve inclusion often result in extreme stress. Research has identified proven inclusion strategies, but there are often problems with implementation in real-world settings. There is a research gap concerning how teachers understand and select interventions. Classic grounded theory methodology was used to identify the primary concern of teachers who were including a student with an EBD in their general education elementary classroom in Alberta, Canada. Constant comparative analysis of 23 teacher interviews revealed a theory of striving for homeostasis by which teachers maintained the classroom’s optimum functioning and addressed the often-conflicting needs of students with an EBD. Teachers used strategies of managing, unothering (fixing), inclusively excluding, recruiting allies, and conserving energy and resources. This theory provides an interpretation of why many current strategies have been ineffective in creating enduring change, given how teachers are perceiving and selecting interventions. Those who support teachers can use the resulting recommendations to reduce stress for teachers and more effectively encourage the use of evidence-based strategies. Such actions may lead to positive social change, promoting teachers’ emotional health and greater school success for this disadvantaged group of students.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".