Locating the Educator in Outdoor Early Childhood Education
Bibliographic record
Abstract
Abstract We tell the story of an experience Kate Dawson, her students, and three eagles had at an outdoor preschool. The experience profoundly affected Kate, and prompted us to ask the following questions: What made this experience feel so magical, and what caused it to happen? Why are thesemagical momentsvaluable, and how might they impact our pedagogical practices? We posit that magical moments in outdoor early childhood education depend upon relational andpathicknowledge, and understandingofplace, rather than intellectual or cognitive knowledgeaboutplace. We suggest conditions and practices educators may employ to foster magical moments, including slow ecopedagogy and embodied, sensory, and spiritual attunement to place. We consider our role as educators in the educator-students-place system, particularly when acknowledging that place is agentic, and acts as learner, knower and teacher. To understand place pedagogically, we must think of ourselves as learners and as the objects of learning, as much as thinking of ourselves asknowers. This requires a pedagogy of embodied responsiveness and a surrender to place as teacher. Far from simplifying the work of the educator, living within a relationship of educator-students-place complexifies the practice of teaching.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".