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Record W4391110323 · doi:10.1177/14779714241227833

Climate change education through drama and social learning: Playful inquiry for building extreme weather events adaptation scenarios

2024· article· en· W4391110323 on OpenAlexaff
Juliano Borba, Michelle Bonatti, Leonardo Medina, Katharina Löhr, Crystal Tremblay, Jutta Gutberlet, Stefan Sieber

Bibliographic record

VenueJournal of Adult and Continuing Education · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTransformative learningDramaAdaptation (eye)Climate changeSociologyConstruct (python library)PerceptionPedagogyPsychologyComputer scienceEcology

Abstract

fetched live from OpenAlex

Considering the projected impacts of climate change in upcoming decades, innovative educational approaches should encourage inventive problem-solving techniques and societal change, fostering transformative climate adaptation. The value of drama in climate adaptation education remains a novel area in the environmental education research literature and requires further exploration of its potential benefits to Climate Change Education (CCE). This article presents a proposal for CCE to include various elements in a drama workshop by evaluating a methodological framework. Participants in the workshop studied the vulnerabilities that arose from flooding and droughts while dramatizing different social conflicts to develop building adaptation scenarios. Through the exploration of problems via playful activities, participants collaboratively construct narratives and texts rich with meaning, based on a critical and creative perception of themes, needs, desires, and overlapping ideologies. This short-term experience manifests efficacy in elucidating the underpinnings of social systems structures, human values, and motivations. This article analyzes workshop results, providing a pedagogical structure and theoretical foundation, contributing to a better comprehension of drama in education and the creation of capacities towards CCE.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.024
GPT teacher head0.299
Teacher spread0.276 · 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 designOther design
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

Citations6
Published2024
Admission routes1
Has abstractyes

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