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Record W1989379736 · doi:10.1177/0267323113491349

Sensationalizing death? Graphic disaster images in the tabloid and broadsheet press

2013· article· en· W1989379736 on OpenAlexaboutno aff
Folker Hanusch

Bibliographic record

VenueEuropean Journal of Communication · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Deviance, and Social Control
Canadian institutionsnot available
Fundersnot available
KeywordsBroadsideNewspaperRace (biology)Media studiesHistoryAdvertisingVisual artsSociologyArtGender studiesBusiness

Abstract

fetched live from OpenAlex

Debates over the extent of graphic imagery of death in newspapers often suffer from generalized assertions that are based on inadequate or incomplete empirical evidence. Newspapers are believed to display death in very graphic ways, with particularly the tabloid press assumedly leading a race to the bottom. This article reports the results of a study of tabloid and broadsheet images of death from the 2010 Haiti earthquake in eight Western European and North American countries. It shows that, far from omnipresent, graphic images of death are relatively rare. While tabloids overall display a larger percentage of graphic images, this was not the case everywhere, with particularly the UK, Canada and the US displaying strong similarities between tabloids and broadsheets. In Austria, Germany, Norway and Switzerland, on the other hand, there were distinct differences between the two types. The article argues that different extents of tabloidization may account for these differences.

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)0.002
metaresearch head score (Gemma)0.016
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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.005
Scholarly communication0.0060.004
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.035
GPT teacher head0.296
Teacher spread0.261 · 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

Citations27
Published2013
Admission routes1
Has abstractyes

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