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Record W3081598966 · doi:10.1088/1748-9326/abb492

Exploration of youth knowledge and perceptions of individual-level climate mitigation action

2020· article· en· W3081598966 on OpenAlexafffund
Gary J. Pickering, Kaylee Schoen, Marta Botta, Xavier Fazio

Bibliographic record

VenueEnvironmental Research Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLikert scalePreparednessScale (ratio)PerceptionSample (material)Climate change mitigationPsychologyClimate changeGreenhouse gasAction (physics)Environmental resource managementPolitical scienceEnvironmental scienceGeographyDevelopmental psychology

Abstract

fetched live from OpenAlex

Abstract The current climate crisis necessitates effective mitigation action across all scales, including behaviours and lifestyle decisions at the individual level. Youth need to align lifestyle with the 2.1 tonnes of CO 2 emissions per person per year required by 2050 to prevent the worse impacts of climate change (CC), yet little is known regarding their preparedness to act nor knowledge of the efficacy of the personal actions available to them. The main objectives of this study were to determine in a representative sample of 17–18 year old Canadians ( n = 487) their: (1) beliefs around whether their activities or lifestyle choices can help to lessen CC, and (2) knowledge of the efficacy of individual-level behaviours in reducing greenhouse gas emissions (GGE). Results from the online survey (Likert scale) show that youth have limited confidence in how well their schooling has prepared them for CC and mitigation. However, the majority (88%) believe that their activities and lifestyle choices can help in mitigating CC. Knowledge of the relative efficacy of GGE-reducing actions was generally poor (Wilcoxon signed rank tests and open-ended responses) with, for instance, recycling overestimated and having one fewer child underestimated, suggesting that youth are not well equipped with the requisite knowledge to maximise CC mitigation through their personal choices. Our findings inform high school curricula and CC education and policy more broadly.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.107
GPT teacher head0.352
Teacher spread0.244 · 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

Citations46
Published2020
Admission routes2
Has abstractyes

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