The First Step Towards Sustainability: The Practices and Implementation of Environmental Education by Educators in Ontario
Bibliographic record
Abstract
Environmental Education is a powerful tool for bringing awareness and promoting future led sustainable goals throughout Ontario. Unpacking its complications and barriers can help us make improvements toward sustainable future objectives. The decisions we make about education facilitate our ability to restore planet Earth (Chiarotto, 2011). The field of environmental education in Ontario is evolving (Kransy, Lundhom & Plummer, 2010) along with pedagogical approaches, which encourage children to participate in global awareness, sustainable living, and active citizenship (Telero, 2004). This research study sought to answer: How do educators implement an environmental education program in an Ontario classroom? Semi-structured interviews were conducted with four educators who currently teach in an elementary or secondary classroom and one administrator at the elementary level. Rich data were gathered, coded, and analyzed using a constructivist framework to explore how educators implement environmental education in classrooms. Various themes emerged. For example, Pedagogical Practices was a predominant theme, such as using experiential outdoor learning, making connections, and promoting change beyond the classroom. This study reveals the importance of implementing environmental education in classrooms that have significant implications for teaching practices.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".