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Record W4321099598 · doi:10.5430/wjel.v13n2p307

A Systematic Literature Review of Tourism Translation and Power

2023· article· en· W4321099598 on OpenAlexvenueno aff
Rong Lu, Muhammad Alif Redzuan Abdullah, Lay Hoon Ang, Che Ghani

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
FundersTianshui Normal University
KeywordsTourismTheme (computing)Systematic reviewPower (physics)Thematic analysisScopusTranslation studiesSociologyPsychologyComputer sciencePolitical scienceQualitative researchLinguisticsSocial scienceMEDLINEWorld Wide Web

Abstract

fetched live from OpenAlex

Despite many studies on translation and power, little has been done to examine the power relations in tourism translation. This systematic literature review aims to investigate publications on tourism translation and power in the translation field. With Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) as the framework, this study employed two primary journal databases in English and Chinese, namely Scopus and CNKI. Following an eight-step guide, a total of eight articles, including seven journals and one book chapter, were included in the final research. Based on systematic literature review and thematic analysis methods, four main themes were identified concerning the power issues in tourism translation. The first theme showed the subjective exertion of power by the translators in tourism translation to promote tourism in a destination. The second theme demonstrated the power exertion in translating tourism texts for economic reasons. The third and fourth themes referred to ideology-related and culture-related power exertion in the process of translating tourism texts. Apart from the emerging themes, the most notable study outcome indicated three research gaps in tourism translation and power, concerning language pairs, translation strategies, and research methodology.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.812
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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