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Record W1467449265

Thematic Organization in English Popular Psychology Texts and Their Corresponding Persian Translations

2014· article· en· W1467449265 on OpenAlexvenueno aff
MahraveSamadi Rahim, Mohsen Askari

Bibliographic record

VenueJournal of academic and applied studies · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTheme (computing)Cohesion (chemistry)PersianLinguisticsThematic structureThematic mapFocus (optics)Thematic analysisComputer scienceFunction (biology)PsychologyQualitative researchSociologySocial scienceWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

Thematic organization plays a fundamental role in the message function of language. An important question is how translators deal with these thematic choices as textual devices when a text is translated into another language. Few studies have unfortunately brought their significance into focus. This study utilized primarily qualitative methods of data collection and analysis to examine the existence of thematic differences in English popular psychology texts and their translations in Persian. The researcher selected four hundred words based on Sical system from the first chapters of ten popular psychology books and investigated thematic development and progression in theme and their Persian translations.Applying Halliday’sthematic organization, the study revealed significant differences and similarities in the original texts and their translations regarding theme types. The study concluded thatthe results of this study can particularly inform translators in their decision-makings while translating in terms of selecting appropriate theme type, conveying the message more clearly as intended by the author, developing cohesion in discourse, and creating a cohesive text.

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 score0.154
Threshold uncertainty score0.311

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 head0.031
GPT teacher head0.303
Teacher spread0.272 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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