With shadows, dust and mud: Activating weathering-with pedagogies in early childhood education
Bibliographic record
Abstract
In response to the perception that climate change is too abstract and its consequences too far-reaching for us to make a difference, recent feminist environmental humanities scholars have drawn attention to connections that can be forged by noticing the intermingling of bodies, relations, materials, places and movements in the world. Inspired by these ideas, Tonya Rooney has proposed that there is potential in working with child–weather relations as a pedagogical response to making climate change more connected and immediate for young children. Mindy Blaise and her colleagues have also shown how ‘matters of fact’ dominate early childhood teaching, and call for new pedagogies that attend to ‘matters of concern’, such as climate change. In this article the authors build on these ideas by drawing also on María Puig de la Bellacasa’s suggestion that we extend our concern to ‘matters of care’ as an ‘ethically and politically charged practice’. The authors report on their work with educators and children in an Australian-based preschool where they have started to engage with matters of concern and matters of care to create new types of pedagogies that they call ‘weathering-with pedagogies’. These are situated, experimental, embodied, relational and ethical, and, the authors suggest, reflect a practice of care, thus providing young children with new ways of responding to climate change. The authors take as their starting point Donna Haraway’s invitation to ‘muddy the waters’ as a way to stir up the possibilities, tensions and challenges in doing such work.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".