STUDENTS’ PERCEPTIONS OF SCHOOLING: THE PATH TO ALTERNATE EDUCATION
Bibliographic record
Abstract
Policies governing education in North America have given schools the responsibility of meeting the needs of a diverse student population, including those with emotional and behavioural difficulties (EBD). To balance their need for individualized programs with their right to inclusion in schools, students with EBD may be placed in alternate programs within a mainstream school setting. However, little is known about student experiences leading to this placement or their experiences in these programs. The purpose of this study was to explore youth’s perceptions of the factors that influenced their being placed in an alternate program for students with EBD. Six eighth-grade students participated in semi-structured interviews and created a visual map of their school trajectories. An interpretative phenomenological analysis (IPA) of the data suggested that their schooling was a tumultuous journey that contributed to their emotional, behavioural, and academic struggles, and to their placement in an alternate school program. Students described disrupted school services, lack of supports, a negative school climate, and disengaging instructional strategies as contributing to their difficulties. An understanding of the influence of school context and policy on student behaviour is necessary if we are to improve educational outcomes and properly support child and adolescent development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".