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Record W7117426110 · doi:10.1080/13504622.2025.2609196

Climate emotions in early childhood: a conceptual framework for research, intervention, and policy action

2025· article· en· W7117426110 on OpenAlexaff
Jane Spiteri

Bibliographic record

VenueEnvironmental Education Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsAction (physics)Environmental educationConceptual frameworkContext (archaeology)Qualitative researchAction research

Abstract

fetched live from OpenAlex

This paper introduces a comprehensive conceptual framework for understanding and addressing climate emotions in early childhood research, policy and practice, while supporting children’s emotional well-being in the face of environmental change. Recognising the growing psychological impact of the climate crisis on young children, the framework posits that climate emotions are shaped by four interdependent domains: Neurodevelopmental/Emotional Regulation Capacities, Caregiver/Educator Co-regulation, Symbolic/Imaginative Mediation, and Sociocultural/Environmental Climate Narratives. The framework moves beyond fragmented and often pathologising concepts like ‘climate anxiety’ and ‘eco-anxiety’ by offering a holistic, developmentally-sensitive view of both adaptive and maladaptive emotional responses. It directly addresses the absence of an integrated, developmentally specific, and cross-system framework for children up to age eight, a gap in the existing climate emotion models. Finally, the framework is translated into actionable policy and intervention strategies for researchers, clinicians, educators, and policymakers, arguing that a systems-level approach is essential to cultivate resilience and promote healthy development in the face of the global climate crisis.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.462
GPT teacher head0.602
Teacher spread0.141 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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