“Rain, Rain, Go Away!” Engaging Rain Pedagogies in Practices With Children: From Water Politics to Environmental Education
Bibliographic record
Abstract
Inspired by the popular children’s song “Rain, Rain, Go Away,” this paper explores what it would look like to consider inviting rain to stay in our practices with children. This invitation acts as a provocation for pedagogical practice that has the potential to engage thinking differently about the ways we work with children and youth. Framed from the vantage point of current curricular practices in environmental education, this paper fuses discussions about water (including racialized and gendered politics) with a consideration of the histories of environmental educational practices as they are currently situated within childhood teaching. In pushing ourselves to think about our bodies as watered/weathered, especially in the context of educational practices, we are able to explore new territory that moves us toward a critique of the taken-for-granted ways in which children and nature are continuously conceptualized, and we open up room for dialogue that moves beyond developmental psychology frameworks. Through considering rain and inviting water to stay in our practices with children, it is suggested that these moments provide critical insight into the more-than-human relationship between children and nature that goes far beyond the romanticized understandings that exist today to consider children’s common worlds.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".