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Record W3011877171 · doi:10.1177/0001699319890902

Actors and justifications in media debates on Arctic climate change in Finland and Canada: A network approach

2020· article· en· W3011877171 on OpenAlexaffabout
Anna Kukkonen, Mark C. J. Stoddart, Tuomas Ylä‐Anttila

Bibliographic record

VenueActa Sociologica · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsMemorial University of Newfoundland
FundersKoneen SäätiöAcademy of Finland
KeywordsCentralityEpistemic communitySustainabilitySovereigntyValue (mathematics)PoliticsGovernment (linguistics)Political scienceArcticSociologyPolitical economyEnvironmental ethicsLawEcology

Abstract

fetched live from OpenAlex

In this paper, we examine the centrality of policy actors and moral justifications in media debates on Arctic climate change in Finland and Canada from 2011–2015. We take a network approach on the media debates by analysing relations between the actors and justifications, using discourse network analysis on a dataset of 745 statements from four newspapers. We find that in both countries, governments and universities are the most central actors, whereas business actors are the least central. Justifications that value environmental sustainability and scientific knowledge are most central and used across actor types. However, ecological justifications are sometimes in conflict with market justifications. Government actors emphasize new economic possibilities in the Arctic whereas environmental organizations demand greater protection of the vulnerable Arctic. Ecological justifications and justifications that value international cooperation are more central in the Finnish debate, whereas justifications valuing sustainability and science, as well as those valuing national sovereignty, are more central in the Canadian debate. We conclude that in addition to the centrality of specific policy actors in media debates, the use of different types of moral justifications also reflects political power in the media sphere.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.516
GPT teacher head0.394
Teacher spread0.123 · 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.

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

Citations20
Published2020
Admission routes2
Has abstractyes

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