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Record W4297183453 · doi:10.1080/13504622.2022.2121381

Knowledge and self-efficacy of youth to take action on climate change

2022· article· en· W4297183453 on OpenAlexaff
Claudia Baldwin, Gary J. Pickering, Gillian Dale

Bibliographic record

VenueEnvironmental Education Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsBrock University
Fundersnot available
KeywordsClimate changeFeelingPsychologyPsychological interventionEnvironmental educationSelf-efficacyPerceptionAction (physics)Positive Youth DevelopmentPolitical scienceSocial psychologyDevelopmental psychologyPedagogyEcology

Abstract

fetched live from OpenAlex

Youth currently in secondary school will spend most of their adult life in a hotter climate associated with climate change (CC). This study investigated attitudes of youth living in an Australian Biosphere Reserve to identify their understanding of CC causes, impact, and mitigation measures, as well as their feelings of self-efficacy and hope. An online survey of 425 youth found that although participants reported a high level of understanding of impacts, they had significant knowledge gaps regarding the most effective measures to mitigate CC. In addition, youth expressed little hope that society will take action, and a lowered sense of self-efficacy. Significant gender and age differences were found for knowledge, experience, and concern about CC impacts, as well as perceptions of how well school had prepared them for navigating the impacts of CC. These findings inform communication and education interventions to mobilise youth to respond to 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.387
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), 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

Citations89
Published2022
Admission routes1
Has abstractyes

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