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Record W2944934404 · doi:10.5539/ells.v9n2p33

Translating and Representing ‘The Aftermath of Daesh’: A Rhetorical Semiotic Study of Some Mosuli Artists’ Works

2019· article· en· W2944934404 on OpenAlexvenueno aff
Ismail Abdulwahhab Ismail

Bibliographic record

VenueEnglish Language and Literature Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsnot available
FundersCurtin University of Technology
KeywordsSemioticsRhetorical questionPaintingSociologyLinguisticsLiteratureHistoryAestheticsArtVisual artsPhilosophy

Abstract

fetched live from OpenAlex

Daesh has profoundly affected the psychological statues of Iraqis; they sacrificed thousands of souls and martyrs to liberate their country from a savage enemy. This study has, therefore, a psychological perspective. It tackles the reflections of agony, suffering and poverty in the behaviour of the artists, writers and translators. Iraqi artists have represented their suffering and pains in their paintings. They encoded the symbols, colours and semiotic mosaics in association with rhetorical connotations. Mosul is the city most affected by the terrorist acts during the war. Therefore, the study has selected four Mosuli artists who drew, painted and visually documented that period. Translation does not limit itself to the study and analysis of verbal/linguistic texts; it also tackles the extra-linguistic signs and codes of the source language to transfer them into the target language appropriately and in a way that seems intelligible to the readers or the spectators of these paintings. The research questions are based on a set of issues: Are the teachers of translation able to construct a bridge between Iraqi society, European society and other societies? These paintings have socio-cultural symbols specific to Iraqi society. Are the teachers of translation able to come out from the shell of the linguistic texts and move towards the semiotic and visual texts?

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.104
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.022
GPT teacher head0.270
Teacher spread0.248 · 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 teacher head, 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
Published2019
Admission routes1
Has abstractyes

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