AJEE Special Issue Ecologising Education: Storying, Philosophising and Disrupting
Bibliographic record
Abstract
Over the last decade there has been a discernable global upsurge of nature kindergartens, forest schools, bush schools and nature-based primary schools; all with varying degrees of intent focused on (re)connecting children and young people in/with/as nature. Yet, not all of these educational endeavours are the same. The understanding of the role the natural world might play in pedagogy varies, the desire to work within the system or radically change it shifts according to commitments and philosophies, and the perceived divide, or lack thereof, between child and nature also has significant effects on curriculum and content. Still, there is a shared commitment among these educational movements to change existing relationships with nature and education. There is a desire and much work being done to ecologise education. In this Special Issue, with its primary focus on the west coast of Canada, we offer a pause to story, philosophise, expand, and disrupt as this ‘type’ of education presents a significant shift and fundamentally questions what school is and what education is for.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".