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Record W3185476141 · doi:10.1007/s11027-021-09963-4

Lifestyle decisions and climate mitigation: current action and behavioural intent of youth

2021· article· en· W3185476141 on OpenAlexafffund
Gary J. Pickering, Kaylee Schoen, Marta Botta

Bibliographic record

VenueMitigation and Adaptation Strategies for Global Change · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMultinomial logistic regressionPsychological interventionClimate changeNormativeReligiosityPsychologyGreenhouse gasSkepticismEnvironmental resource managementSocial psychologyEnvironmental sciencePolitical scienceEcology

Abstract

fetched live from OpenAlex

Abstract Youth carry the burden of a climate crisis not of their making, yet their accumulative lifestyle decisions will help determine the severity of future climate impacts. We surveyed 17–18 year old’s ( N = 487) to establish their action stages for nine behaviours that vary in efficacy of greenhouse gas emission (GGE) reduction and the explanatory role of climate change (CC) knowledge, sociodemographic and belief factors. Acceptance of CC and its anthropogenic origins was high. However, the behaviours with the greatest potential for GGE savings ( have no children/one less child, no car or first/next car will be electric, eat less meat ) have the lowest uptake. Descriptive normative beliefs predicted intent to adopt all high-impact actions, while environmental locus of control, CC scepticism, knowledge of the relative efficacy of actions, religiosity and age were predictive of action stage for several mitigation behaviours (multinomial logistic regression). These findings inform policy and communication interventions that seek to mobilise youth in the global climate crisis response.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.422

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 communication0.0000.000
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 head0.098
GPT teacher head0.326
Teacher spread0.228 · 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 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

Citations31
Published2021
Admission routes2
Has abstractyes

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