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Record W2036264285 · doi:10.1002/wcc.272

Perceptions of time in relation to climate change

2014· article· en· W2036264285 on OpenAlexaff
Sabine Pahl, Stephen R.J. Sheppard, Christine Boomsma, Christopher Groves

Bibliographic record

VenueWiley Interdisciplinary Reviews Climate Change · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of British Columbia
FundersEngineering and Physical Sciences Research Council
KeywordsClimate changePerceptionPerspective (graphical)Context (archaeology)Relation (database)Political economy of climate changeSociologyPsychologySocial psychologyEcologyGeographyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Time is at the heart of understanding climate change, from the perspective of both natural and social scientists. This article selectively reviews research on time perception and temporal aspects of decision making in sociology and psychology. First we briefly describe the temporal dimensions that characterize the issue of climate change. Second, we review relevant theoretical approaches and empirical findings. Then we propose an integration of these insights for the problem of climate change and discuss mismatches between the human mind, surrounding social dynamics, and climate change. Finally, we discuss the implications of this article for understanding and responding to climate change, and make suggestions on how we can use the strengths of the human mind and social dynamics to communicate climate change in its temporal context. This article is categorized under: Climate, History, Society, Culture > Ideas and Knowledge Perceptions, Behavior, and Communication of Climate Change > Perceptions of Climate Change

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)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.008
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.337
GPT teacher head0.461
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations182
Published2014
Admission routes1
Has abstractyes

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