Two stories of environmental learning and experience
Bibliographic record
Abstract
This paper highlights action research into the practices of environmental learning through two interconnected stories focusing respectively on educational policy and the details of classroom instruction. Together these illustrate how a framework guides teachers in educational planning and supports the implementation of a curriculum for environmental learning in diverse subjects. Teacher inquiry, focus groups and interviews informed a collaborative writing process involving teachers and academics. The framework offers a conceptual view for environmental learning in all settings providing principles of teaching and learning to guide teachers in activities in a variety of learning contexts. The broader study sets the scene and the context for imbedded teacher inquiry. This study provides a personal perspective on how environmentally focused lessons were developed and researched by teachers. It highlights the story of a Grade 2/3 teacher (Ms. P) as she embarks on a program of action research about outdoor learning exemplary of other elements of the imbedded action research in the broader study. Multiple, overlapping themes emerge as she documents her reflections and students’ interactions with local environments. This paper and its narratives together relate how the concepts of environmental learning and teacher experience empower us to guide learning in new, exciting ways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".