MétaCan
Menu
Back to cohort
Record W4366420732 · doi:10.1080/13504622.2023.2202368

Effects of vicarious experiences of nature, environmental beliefs, and attitudes on adolescents’ environmental behavior

2023· article· en· W4366420732 on OpenAlexaff
Yuyu Sun, Xiaoxu Lu, Jian Cui, Ke Du, Shumin Xie

Bibliographic record

VenueEnvironmental Education Research · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMediationPsychologyStructural equation modelingEnvironmental educationSocial psychologyDevelopmental psychologySociology

Abstract

fetched live from OpenAlex

This study explores the relationship between vicarious experiences of nature, environmental behavioral beliefs, environmental attitudes and adolescents’ environmental behavior. Based on a sample of 1476 adolescents from five high schools in Jiangsu Province, China, we proposed a model that was supported by structural equation modelling analysis. The model suggests that vicarious experiences of nature are positively associated with adolescents’ environmental behavior. Environmental attitudes and environmental behavioral beliefs play partial mediator roles in the relationship of vicarious experiences of nature and environmental behavior. In addition to the significant direct impact of vicarious experiences of nature on environmental behavior, indirect effects are also achieved through behavioral beliefs, environmental attitudes and the serial multiple mediation effect of the two variables. These research results imply that we must pay attention to the important role of vicarious experiences of nature in cultivating adolescents’ environmental behavior. In addition, the theoretical and practical implications of this research are discussed, as well as the limitations and potential for future research.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score1.000

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 studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.333
Teacher spread0.324 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Education ResearchSame topicEnvironmental Education and SustainabilityFrench-language works237,207