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Record W4400666070 · doi:10.1080/17524032.2024.2377719

Instagram as an Arena of Climate Change Communication and Mobilization: A Discourse Network Analysis of COP26

2024· article· en· W4400666070 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Communication · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of British ColumbiaMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMobilizationClimate changeSocial mobilizationPolitical scienceSociologyPoliticsLawEcology

Abstract

fetched live from OpenAlex

Conference of the Parties (COP) meetings on climate change are opportunities for social movements and other non-state actors to engage in climate change communication and mobilization. We focus on Instagram as an online arena of climate communication and mobilization during COP26, held in Glasgow. Instagram is a distinct arena for eco-political communication and activism because of its visual focus and generally younger user base. Using a Discourse Network Analysis approach, we analyse 2417 posts to examine relationships and alignments across imagery, discourses, and actors. Instagram serves as a space to articulate critical counter-discourses of climate justice, Indigenous rights, and individual action as a response to perceived failures of COP processes and formal agreements. At the same time, images of celebrities and politicians structure much of the Instagram discourse network. This highlights how Instagram contributes to a celebritization of climate politics, with individual political actors positioned as climate heroes or villains.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.185
GPT teacher head0.428
Teacher spread0.243 · 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