Wilding Pedagogies: Theorising, Practising and Imagining towards a Changing, Decolonising and Reconciling World
Bibliographic record
Abstract
We acknowledge and express our deepest respect for Country, along with its ancestors and the descendants of the Lands on which we gather, research, teach and wild.We recognise that the Australian Journal of Environmental Education is one of the oldest internationally refereed journals in environmental education and is located within the region of the world's oldest continuing living culture -Aboriginal and Torres Strait Islander Peoples.Who made the world?Who made the swan, and the black bear?Who made the grasshopper?This grasshopper, I meanthe one who has flung herself out of the grass, the one who is eating sugar out of my hand, who is moving her jaws back and forth instead of up and downwho is gazing around with her enormous and complicated eyes.Now she lifts her pale forearms and thoroughly washes her face.Now she snaps her wings open, and floats away.I don't know exactly what a prayer is.I do know how to pay attention, how to fall down into the grass, how to kneel down in the grass, how to be idle and blessed, how to stroll through the fields, which is what I have been doing all day.Tell me, what else should I have done?Doesn't everything die at last, and too soon?Tell me, what is it you plan to do with your one wild and precious life?(Mary Oliver, The Summer Day, 1992)
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".