I Hate the Global Warming Factory! Caring for Tadpoles During the Climate Emergency
Bibliographic record
Abstract
In this paper we share the story of our participatory action research project to create a lunchtime program to support elementary school students in building reciprocal relationships with the land, enhancing collective wellness. An emergent pond that suddenly dried up due to unseasonably warm temperatures, leaving tadpoles stranded, became the focus of much of our learning. The children worked tirelessly to restore the pond and care for the tadpoles. Through this research, we learned how impactful environmental education can be when it is guided by love (verses logic), involves thinking with (rather than thinking about) more-than-human kin, and when children actively participate in knowledge creation and mobilization through digital storytelling. Our study illustrates how action-research serves as a generative approach to participatory planetary health, inspiring both individual and collective action to address the multifaceted environmental crisis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".