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Record W2903890557 · doi:10.1177/0741088318804822

How Do Online News Genres Take Up Knowledge Claims From a Scientific Research Article on Climate Change?

2018· article· en· W2903890557 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

VenueWritten Communication · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRhetorical questionNews mediaClimate changeNews valuesAffect (linguistics)Media studiesSociologyPolitical scienceLiteratureArt

Abstract

fetched live from OpenAlex

The Internet has helped to change who writes about science in the news, how news is written, and how it is taken up by different audiences. However, few studies have examined how these changes have impacted the uptake of scientific claims in online news writing. This case study explores how online news genres take up knowledge claims from a research article on climate change over a period of one year and shows how shifting boundaries between rhetorical communities affect genre uptake. The study results show that online news writers predominantly use the news report genre to cover research findings for 48 hours, after which they predominantly use the news editorial genre to engage these findings. Analysis suggests that the news report genre uses the press release and the article abstract as intermediary genres, but the news editorial uses only the abstract. I argue that the switch between genres repositions the scientist, the journalist, and the public epistemologically, a reorientation that favors uptake in news media outlets supporting action to mitigate climate change and its effects.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.725
GPT teacher head0.539
Teacher spread0.186 · 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