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Record W4410826352 · doi:10.1111/pops.70045

Compensating personal climate response inefficacy with political conservatism?

2025· article· en· W4410826352 on OpenAlexafffund
Xiaobin Lou, Liman Man Wai Li, Kenichi Ito

Bibliographic record

VenuePolitical Psychology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of Lethbridge
FundersUniversity of Lethbridge
KeywordsConservatismPoliticsPsychologySocial psychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract People grapple with the reality that their individual actions are too insignificant to impact climate change—a phenomenon known as personal climate response inefficacy. The compensatory control theory articulates that people may turn to overarching social institutions or ideologies (e.g., conservatism) when they lack personal control. Inspired by this theory, the present research explored whether individuals experiencing personal climate response inefficacy are more likely to embrace political conservatism. Study 1 analyzed data from the European Social Survey ( N = 44,387) and provided preliminary evidence of a positive association between personal climate response inefficacy and political conservatism. Study 2 utilized four‐wave longitudinal data from 1008 American participants and identified a consistent within‐person cross‐lagged effect, where personal climate response inefficacy predicted increased political conservatism over time. Both studies ruled out the potential conflating effect of the perceived threat of climate change. Study 3 collected cross‐sectional data from 270 Americans and replicated the major findings while controlling for the potential confounding effect of general self‐efficacy. Study 4 ( N = 261) manipulated personal climate response inefficacy yet only provided partial supportive evidence for our main findings. These pieces of evidence suggest that psychological processes specific to climate change can have spillover effects on other domains.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.426
GPT teacher head0.550
Teacher spread0.124 · 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 designTheoretical or conceptual
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

Citations2
Published2025
Admission routes2
Has abstractyes

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