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Record W1991863508 · doi:10.1080/17524032.2014.909509

Global Journalism in Decision-Making Moments: A Case Study of Canadian and American Television Coverage of the 2009 United Nations Framework Convention on Climate Change in Copenhagen

2014· article· en· W1991863508 on OpenAlexaboutno aff
Magda Konieczna, Kristine Mattis, Jiun-Yi Tsai, Xuan Liang, Sharon Dunwoody

Bibliographic record

VenueEnvironmental Communication · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsnot available
Fundersnot available
KeywordsJournalismFraming (construction)ConventionPolitical scienceClimate changeGlobal warmingUnited Nations Framework Convention on Climate ChangeNews mediaNews valuesMedia studiesSociologyGeographyLaw

Abstract

fetched live from OpenAlex

Climate change is a phenomenon with global causes but local effects, and thus global climate change decision-making moments provide ideal opportunities to examine how local and global discourses work together—or do not—through global journalism. This case study investigates the globally focused vs. culturally bound frames used in television news coverage, in Canada and the USA, of the 2009 United Nations Climate Change Conference in Copenhagen. Initial quantitative findings that Canadian media used many more culturally bound sources than did American media contradict the past findings and suggest Canadian media engaged less in producing global journalism than did American media. A follow-up qualitative analysis not only found more global framing in the American stories, but also concluded that global sources did not necessarily create global journalism; instead, a global orientation is required.

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)0.015
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 score0.149
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.007
Science and technology studies0.0380.012
Scholarly communication0.0100.003
Open science0.0020.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.126
GPT teacher head0.409
Teacher spread0.283 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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