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Record W3002125664 · doi:10.1080/09644016.2019.1708538

Climate change risk perceptions and the problem of scale: evidence from cross-national survey experiments

2020· article· en· W3002125664 on OpenAlexafffund
Endre Tvinnereim, Ole Martin Lægreid, Xiaozi Liu, Daigee Shaw, Christopher P. Borick, Érick Lachapelle

Bibliographic record

VenueEnvironmental Politics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaNorges Forskningsråd
KeywordsClimate changeOptimismOptimism biasSurvey data collectionPerceptionScale (ratio)Empirical evidenceGeographySurvey researchConfoundingPsychologyDemographic economicsSocial psychologyEconomicsApplied psychology

Abstract

fetched live from OpenAlex

We examine the concept of spatial optimism, defined as the tendency for individuals to perceive climate change as less threatening to themselves than to people in geographically more distant locations. Existing studies find mixed evidence of this phenomenon, while the methods employed often fail to rule out confounding factors. To resolve these empirical and methodological tensions, we present results from a survey experiment fielded in nine countries spanning Europe, North America, and Asia. The survey finds that respondents systematically perceive climate change as a greater threat to the world than to themselves, in nine countries. However, while groups that may be considered more vulnerable to climate change often display higher levels of perceived overall risk, the survey finds evidence of spatial bias to be systematic across and within cases. Future research should apply this measurement strategy in more vulnerable countries and over time.

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)0.120
metaresearch head score (Gemma)0.343
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 score0.120
Threshold uncertainty score0.633

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.343
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.005
Scholarly communication0.0030.005
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.446
GPT teacher head0.448
Teacher spread0.002 · 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

Citations35
Published2020
Admission routes2
Has abstractyes

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