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Record W4380569745 · doi:10.6000/1929-4409.2020.09.214

Semiotics in Political Discourse. An Analytical Treatment of Political Text Criticisms Since 2003: The Case of the Discourse of Prime Minister Nouri Al-Maliki

2022· article· en· W4380569745 on OpenAlexvenueno aff
Jasim Muna Arif, Hadi Nahla Jawad

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSemioticsSign (mathematics)PoliticsMeaning (existential)LinguisticsContext (archaeology)SociologyEpistemologyStyle (visual arts)Function (biology)Social sciencePolitical scienceLiteratureLawPhilosophyHistoryMathematicsArt

Abstract

fetched live from OpenAlex

This article discusses the function of semiotics in political discourse after the socio-political processes taking place in Iraq since 2003 and its role in the development of textual criticisms of some Iraqi politicians, analyzes the reasons for its functioning in the speech of politicians. The research is mainly focused on finding out to what extent political text studies draw on sign systems that can store and transmit information, the nature of its purpose and the use of available fields for the purpose to be achieved. The chief purpose of the study is to investigate and also clarify the symbols and signs appear within the framework of discursive Iraqi politicians, the nature of the symbols used, and the meanings that are included in the discourse in terms of structure, context, and form. Moreover, it has been attempted to define semiotic features in the texts of Iraqi politicians; and elicit structure, style and wording, and the degree of convergence of meaning and form in the semiotic application.

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)0.008
metaresearch head score (Gemma)0.010
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 score0.014
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0140.042
Scholarly communication0.0130.008
Open science0.0010.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.387
Teacher spread0.317 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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Same venueInternational Journal of Criminology and SociologySame topicDiscourse Analysis in Language StudiesFrench-language works237,207