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Record W7062515410

Understanding the Dimensions of Climate Change Misinformation

2024· other· en· W7062515410 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBrock University Digital Repository (Brock University) · 2024
Typeother
Languageen
FieldEngineering
TopicAdvanced Power Generation Technologies
Canadian institutionsBrock University
Fundersnot available
KeywordsMisinformationClimate changeMainstreamConfusionSocial mediaWork (physics)Foundation (evidence)Focus (optics)
DOInot available

Abstract

fetched live from OpenAlex

Climate change misinformation (CCM) is emerging as one of the most pressing barriers to climate action. Referring to false or inaccurate information about climate change, CCM threatens to cast confusion on both the severity and existence of climate change. As CCM has permeated into mainstream news and social media platforms, it can now reach larger audiences and decrease support for climate change mitigation practices and policies. To combat CCM effectively, more work is needed to understand it as one unified concept. This major research paper focuses on filling this gap by identifying the dimensions of CCM through an inductive content analysis of peer-reviewed literature. Utilizing an inductive approach, five overall dimensions of CCM were synthesized: attributes, psychology, politics, disinformation, and responses. These dimensions establish the necessary foundation to understand CCM as one concept, increase global resiliency to CCM, and develop strategies that focus on eliminating CCM in the future.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.190
Teacher spread0.166 · 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