Schooling Ecologically: An Inquiry Into Teachers’ Ecological Understanding in ‘Alternative’ Schools
Bibliographic record
Abstract
Abstract This article reports on an inquiry into ecological understanding and the professional practice of a selection of teachers in alternative and/or independent non-systemic schools in Australia, Canada and the United States. Through a reflective, participatory framework, based on the premise that it is one thing to observe ‘an ecology’, another to understand one's self as part of it, as actively involved in ‘bringing forth our world’, the project sought to understand if and how teachers employ systemic, ecological insights in their teaching. The project looked at the underlying ecological principle of ‘connection’ and how teachers work with this, through teacher education and options for further education in ecological understanding, at the responsibilities schools hold for ecological understanding, and at ways in which individual teachers have worked with this form of knowledge. Data was gathered through semi-structured interviews with small numbers of teachers in five schools. The philosophical underpinnings of these schools were considered in relation to the teachers’ capacities to facilitate ecological understanding and the organisational setting in which these schools operate. Teacher perspectives are reported and discussed through a structured presentation of selected responses to a series of questions on the overlapping themes of ecological insight and formal and informal learning processes.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".