Meeting Different Needs: Balancing Environmental and Special Education in Ontario's Elementary Classrooms
Bibliographic record
Abstract
Research in the field of special and environmental education (EE) gained support in the 1980s, and the studies that have been conducted since then have explored the effects of Outdoor Education Centre (OEC) visits for students with special needs in other countries (Dominguez & Schilling, 2001; Volk & Cheak, 2003; Berger, 2006), but studies on Canadian students and in- class environmental instruction are currently lacking. This qualitative case study uses a review of relevant literature and three semi-structured interviews with certified teachers to explore educator self-efficacy in creating, integrating and differentiating EE programming for students with special needs in elementary classrooms in Ontario, Canada. Findings from the study reveal the dynamics of teaching for life skills, the importance of outdoor learning and parental involvement to increase school-wide support for EE, and how universal design for learning is a best practice for EE differentiation. This study uniquely contributes to a greater body of research that has important implications for reforms in education, especially in relation to school-based, diverse ability settings in Ontario.
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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 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".