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Record W2167322003 · doi:10.1177/0963662511414983

Working the fringes: The role of letters to the editor in advancing non-standard media narratives about climate change

2011· article· en· W2167322003 on OpenAlexaffabout
Nathan Young

Bibliographic record

VenuePublic Understanding of Science · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsScrutinyNarrativeSkepticismNewspaperNormativeClimate changeMedia studiesSubject (documents)Political scienceNeutralitySociologyEpistemologyLawLiteratureArtComputer scienceLibrary sciencePhilosophy

Abstract

fetched live from OpenAlex

This article examines the role of letters to the editor in advancing and sustaining non-standard narratives about climate change in the print media. The letters page is a unique section of the newspaper that is subject to distinct functional and normative pressures. It is also a place where standard media norms are weakest and non-journalistic narratives have an opportunity to leak in. Using research into climate change coverage in eight major Canadian dailies in 2007-2008, the article employs content analysis and critical discourse analysis to examine how letters advance fringe arguments into the print media landscape that would not stand up to regular journalistic scrutiny. While these arguments come from all sides of the issue, it is argued that letters are particularly important for establishing and legitimizing conservative-skeptical perspectives on 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)0.030
metaresearch head score (Gemma)0.147
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.147
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0300.021
Scholarly communication0.0300.012
Open science0.0020.008
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.413
GPT teacher head0.384
Teacher spread0.030 · 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.

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

Citations40
Published2011
Admission routes2
Has abstractyes

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