MétaCan
Menu
Back to cohort
Record W2958196625 · doi:10.5539/ijel.v9n4p326

Discourse Analysis: New Language and New Attitude towards Yemen in Contemporary British Novel; Salmon Fishing in Yemen as an Example

2019· article· en· W2958196625 on OpenAlexvenueno aff
Mubarak Altwaiji

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsnot available
FundersNorthern Border University
KeywordsOrientalismMisrepresentationRepresentation (politics)PoliticsNarrativeFishingNewspaperSociologyHarmony (color)Media studiesHistoryPolitical scienceLiteratureLawArt

Abstract

fetched live from OpenAlex

In the critical work on European orientalism, the European scholars approach post 9/11 British neo-orientalist discourse with a totalizing view of representation; a part of the dominant misrepresentation. This study examines issues related to Yemen in Paul Torday’s novel Salmon Fishing in the Yemen (2007). In Salmon Fishing, Torday uses fragmented forms of narrations for his new approach of representation. He uses newspapers, interviews, emails, news articles, document transcripts, diary entries, personal interviews, scientific reports and memoranda as narrative techniques to re-conceptualize the Yemeni people. This study investigates the British political and cultural attitudes towards Yemen and the improvement in the representation of Yemen in post 9/11 British discourse by focusing on the fissures between classic orientalism and neo-orientalism. In the analysis of Salmon Fishing, the study scrutinizes the views of Ralph Emerson and Georg Lukács which are usually associated more closely with studies on representations. The study manifestly identifies the harmony, cooperation and mutual understanding between the east and the west in post 9/11 British discourse on Yemen.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
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 head0.055
GPT teacher head0.352
Teacher spread0.297 · 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.

Study designObservational
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

Explore more

Same venueInternational Journal of English LinguisticsSame topicMiddle East and Rwanda ConflictsFrench-language works237,207