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Record W3208817727 · doi:10.32996/ijllt.2021.4.10.3

The Impact of Cultural and Translational Studies on Modern Comparative Literature Studies

2021· article· en· W3208817727 on OpenAlexaff
Azadeh Mehrpouyan, Elahesadat Zakeri

Bibliographic record

VenueInternational Journal of Linguistics Literature & Translation · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComparative literatureTranslation studiesCultural studiesLiterary criticismConversationCultural diversitySociologySocial scienceLinguisticsAnthropologyPhilosophyCommunication

Abstract

fetched live from OpenAlex

Modern comparative literature with globalization phenomenon extends linguistic and political boundaries, even for conserving and revitalizing languages particularly minor languages with cultural and ethnic exchanges. Such this emergence of comparative literature might return from contemporary translational and cultural studies as crucial and effective factors in the study of comparative literature. The role, relationship, and impact of translation and cultural studies on modern comparative literature are explored via a descriptive analysis. Translational and cultural studies in current comparative literature studies facilitate the relevant studies and they play a supplementary role for literary study. This study confirms a significant relationship exists among contemporary translational, cultural, and literary works intangibly and inevitably that helps to study comparative literary works. The findings report cultural and translational studies can be fruity informing literary studies, new writing styles besides intercultural conversation; nevertheless, scholars of comparative literature have argued that their discipline has been significantly subsumed and substituted by translation studies. The results indicate contemporary translation and cultural studies have paved the way for comparative literature researchers to achieve cultural knowledge and to strengthen the culture with developing national literature.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.743
Threshold uncertainty score0.494

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 head0.164
GPT teacher head0.434
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 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
Published2021
Admission routes1
Has abstractyes

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