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Record W2902712663 · doi:10.1111/ssqu.12563

Political Leaning and Coverage Sentiment: Are Conservative Newspapers More Negative Toward Women?*

2018· article· en· W2902712663 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

VenueSocial Science Quarterly · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Influence and Politics
Canadian institutionsMcGill University
Fundersnot available
KeywordsNewspaperPoliticsTone (literature)Cover (algebra)Political scienceMedia coveragePropositionAdvertisingSociologyMedia studiesLawBusinessEngineeringLinguistics

Abstract

fetched live from OpenAlex

Objectives This article examines whether newspapers’ political leaning affects coverage tone for individuals in the news and whether the gender of the person covered affects this relationship. Methods I analyze sentiment data on millions of person‐names from more than 200 major American newspapers between the years 2004 and 2009, juxtaposing them with various measurements for the political leaning of these newspapers. Results Results show mixed support for the idea that political leaning in the media affects coverage patterns for individuals in the news. While newspapers located in states that are more likely to vote for Republicans cover women in a more negative way, I find no relationship between political leaning scores and coverage sentiment for men. Conclusions The study shows mild support for the proposition that relatively liberal newspapers are more likely to cover women and women's issues in a positive way.

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 categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.999

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 studies0.0030.011
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.344
Teacher spread0.316 · 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