MétaCan
Menu
Back to cohort
Record W2976784473 · doi:10.18357/jcs00019173

“Rain, Rain, Go Away!” Engaging Rain Pedagogies in Practices With Children: From Water Politics to Environmental Education

2019· article· en· W2976784473 on OpenAlexvenueno aff
Ashley Do Nascimento

Bibliographic record

VenueJournal of Childhood Studies · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsSituatedPoliticsContext (archaeology)Environmental educationSociologyPedagogyEarly childhood educationPolitical scienceGeographyArchaeologyLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.292
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Childhood StudiesSame topicEnvironmental Education and SustainabilityFrench-language works237,207