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Record W2609153695 · doi:10.1515/nor-2017-0415

The Iconic Image in a Digital Age

2017· article· en· W2609153695 on OpenAlexaboutno aff
Mette Mortensen, Stuart Allan, Chris Peters

Bibliographic record

VenueNordicom review/NORDICOM review · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperReflexivityPhotojournalismNarrativeSalientNormativeMedia studiesSociologySet (abstract data type)PerceptionSocial mediaDigital mediaPeriod (music)HistoryVisual artsAestheticsPsychologyPhotographySocial scienceArtPolitical scienceLiteratureLawComputer science

Abstract

fetched live from OpenAlex

Abstract This article investigates selected newspapers’ editorial mediations over contrasting perceptions regarding the significance of a controversial set of ‘iconic’ news photographs, namely images of Alan Kurdi, a three-year-old Syrian refugee, whose drowned corpse washed ashore in September, 2015. Specifically, this study examined individual editorial items, published by leading Danish, Canadian and British newspapers over a four-month period, engaging with and reflecting upon this imagery. Our analysis revealed several key deliberative features of editorial self-reflexivity, with three especially salient themes shown to be emergent across the coverage: a) instantaneousness and historical photographic precedents; b) social media’s perceived influence on photojournalism; and c) normative associations of affective qualities for this imagery. By elucidating these features of editorial self-reflexivity within a convergent digital media ecology, this article offers original insights into how and why the epistemic values governing visual communication are being reconsidered and redrawn under pressure from institutional imperatives.

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.010
Scholarly communication0.0070.003
Open science0.0010.002
Research integrity0.0010.001
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 head0.048
GPT teacher head0.318
Teacher spread0.270 · 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 designNot applicable
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

Citations34
Published2017
Admission routes1
Has abstractyes

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