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Record W4362575023 · doi:10.22215/etd/2023-15402

Eco-Anxiety in Daily Life: Relationships with Well-Being and Pro-Environmental Behaviour

2023· dissertation· en· W4362575023 on OpenAlexafffund
Paul Lutz

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWorryAnxietyPsychologyAffect (linguistics)Trait anxietyFeelingMeaning (existential)TraitClinical psychologyDevelopmental psychologySocial psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Little is known about how eco-anxiety, or feelings of anxiety and worry about mounting environmental issues, relates to well-being and pro-environmental behaviour in daily life.To help address this issue, I conducted a preregistered daily diary study, wherein Carleton University undergraduates (N = 132) provided trait reports and two weeks of daily reports (n = 1439) on eco-anxiety, positive and negative affect, meaning in life, and pro-environmental behaviour.At the trait level, average scores on eco-anxiety were fairly low; yet, higher scores were associated with less positive affect and more negative affect and pro-environmental behaviour.Daily average scores on eco-anxiety were even lower at the state level, but on days people did feel greater eco-anxiety, they also reported greater negative affect and proenvironmental behaviour.Lagged analyses provided some evidence that eco-anxiety increases future negative affect.No significant relationships between eco-anxiety and meaning in life emerged at both levels of analysis.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.234
Teacher spread0.227 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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