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Record W7071010843

Religion on an Ordinary Day: An International Study of News Reporting

2021· article· en· W7071010843 on OpenAlexaboutno aff

Bibliographic record

VenueKeele Research Repository (Keele University) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamSecularizationNewspaperChristianityPoliticsRepresentation (politics)Diversity (politics)Religious diversitySample (material)Qualitative researchContent analysis
DOInot available

Abstract

fetched live from OpenAlex

This article provides an introduction to an international study of religion on an ‘ordinary day’ in the news. Taking as its sample newspapers in the UK, Finland, Australia and Canada on 17 September in 2013, 2014 and 2015, the study aimed to provide a systematic analysis of ordinary or everyday coverage of religion in the news, providing an important contribution to research on religion in media, which tends to focus on specific events and controversies. Using both quantitative and qualitative methods to examine global, national and local stories about conventional, common religion and the secular sacred, the study also provides insights into conducting multi-national and interdisciplinary projects. While the findings demonstrate a fairly standardized representation of religion in mainstream news, with cultural Christianity dominating, the varying national and political contexts throw up some interesting specificities relating to increasing diversity and secularization experienced within wider processes of globalization.

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)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0050.006
Scholarly communication0.0080.007
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.121
GPT teacher head0.433
Teacher spread0.313 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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