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Record W2555740502 · doi:10.7202/1037871ar

Images de l’islam. Représentations de l’islam et des musulmans dans les journaux télévisés flamands

2016· article· fr· W2555740502 on OpenAlexvenueno aff
Elke Ichau, Leen d’Haenens

Bibliographic record

VenueDiversité urbaine · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesIslamPolitical sciencePhilosophyTheology

Abstract

fetched live from OpenAlex

Cette étude s’intéresse à la manière dont les journaux télévisés flamands présentent l’islam et les musulmans au moyen d’une analyse de contenu quantitative et qualitative d’un échantillon de 100 sujets d’actualité diffusés par une chaîne publique et une chaîne commerciale en 2010. Nos résultats montrent que l’islam et les musulmans n’ont pas fait l’objet d’une grande attention médiatique en 2010. La majorité des sujets axés sur l’islam concernent des événements survenus à l’étranger, principalement en Europe et aux États-Unis. Les thèmes les plus récurrents sont le terrorisme et la religion. Les sujets consacrés spécifiquement à l’islam en tant que religion sont généralement assez complets, présentant une information de fond, différents points de vue et différentes sources. La plupart des sujets portent toutefois sur d’autres questions qui associent souvent, de manière plus ou moins explicite, l’islam et les musulmans à des thématiques négatives, telles que le terrorisme et la violence.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
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 head0.030
GPT teacher head0.331
Teacher spread0.301 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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