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Record W4226397838 · doi:10.22051/jlr.2021.35142.1998

Frame-based approach in translating cultural elements: A case study of the Persian dubbing of Charming

2023· article· en· W4226397838 on OpenAlexaboutno aff
Ahmad Iranmanesh

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPersianFrame (networking)LinguisticsComputer scienceTelecommunicationsPhilosophy

Abstract

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INTRODUCTIONConceptual frames are packages of knowledge that represent the worldview of people, including beliefs, values, emotions, prototypes of people and objects, order of events in particular situations, social scenarios, permissible thought structures,and metaphors and metonyms (Fillmore & Baker, 2009). All the experiences and assumptions that we have about the background as well as appendices of our experiences are rooted in our conceptual frames. Accordingly, we have a schematic knowledge of various issues, such as marriage, government, religion, weekends, military ranks, colors, and the like. Frames are based on our knowledge of a phenomenon and its cultural implications, and have little to do with words (that is why it is difficult to understand a joke that belongs to an unfamiliar culture). As a result, mere attention to linguistic forms prevents a comprehensive understanding of the text. Meanwhile, each word invokes a frame and represents a part or aspect of that frame and at the same time the very same semantic frame provides the background knowledge with which we understand the meaning of relevant words. For example, understanding words such as principal, teacher, and student requires activating the frame of "school". Additionally, a particular word causes the audience to focus on a specific part of the frame and consider its scenario from a certain angle. Therefore, invoking a frame is a cognitive act by which the interpreter (almost unconsciously) understands the input information.The present study aimed at studying the use of conceptual frames in rendering the cultural elements of Charming, in its Persian dubbed version. Accordingly, the following questions are posed:(i) Which conceptual frames have been used to express cultural elements in Charming?(ii) Which translation strategies have been used to render cultural elements in the Persian dubbed version of Charming?In the meantime, the study holds the following two hypotheses:(i) There is a significant difference between the conceptual frames in the original version of Charming and its Persian dubbed version.(ii) The differences are rooted not only in cultural dissimilarities between the two languages ​​but in the personal choices made by the translator. MATERIALS AND METHOOLOGYThe present research, using the descriptive-analytical method, aims at investigating the application of conceptual frames in rendering cultural elements to provide more practical methods in translation of culture specific items. The corpus of this research consisted of an America-Canadian animation, namely Charming and its Persian dubbed version by Nama Ava. Charming is a 2018 Canadian-American computer-animated musical comedy film which is a rich source of a wide variety of conceptual frames (e.g., the postmodernist attitude in making the animation gave it a considerable capacity to trigger the pre-existed frames in the mind of its audience under intertextuality). On the other hand, the Persian dubbed version of the animation is taken from Nama Ava which is among the most authentic Persian internet sites providing translation services for audio-visual products. For comparing purposes, firstly, the classification for conceptual frames as well as definitions for each type were presented and discussed under the taxonomy proposed by Lopez (2002). According to her, the main categories of frames include visual, situational, text-type, social, institutional and generic frames. Secondly, and after recognizing the frames in the original version of charming, they were matched with their (possible) counterpart Persian frames proposed by the translator.Thirdly and based on a comparison between the original English frames and their equivalences in the Persian dubbed version, the strategies applied by the translator to render the recognized and discussed cultural elements. RESULTS AND DISCUSSIONThe results indicated that the most frequent frames in the English version of Charming were social (126 cases), institutional (41 cases), text type (33 cases), situational (29 cases), generic (11 cases) and visual frames (9 cases). In the meantime, the strategies used by the translator to render the original frames in the Persian dubbed translations were transliteration, loan translation, addition, description, omission and cultural equivalent among which addition as the least and cultural equivalent as the most frequent strategies were recognized. The findings also show that the dominant attitude in transferring the source frames in the Persian dubbing is domestication. CONCLUSIONThis paper concluded that, firstly, there is a considerable difference between the conceptual frames in the original version of Charming and its Persian dubbed version. Accordingly, out of 249 cases of conceptual frames in Charming, 174 cases have been rendered differently or have not been rendered at all. Secondly, the reason behind this lack of transfer or different rendering of the original frames in the Persian dubbed version is twofold: a) differences between English and Persian culture, and b) the choices made by the translator despite the similarity of frames between the two cultures. Here, the translator has preferred to use a different conceptual frame, remove the source frame, add the TL frame to the source text, or neutralize the cultural element of the source frame in the TL. That is to say the translator, instead of focusing solely on linguistic forms of the SL, has tried to render the function of the original by activating appropriate frames of the Persian culture in the mind of the TL audience or omitting the SL frame for cultural reasons.

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)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.037
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0260.014
Scholarly communication0.0070.005
Open science0.0040.006
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.498
GPT teacher head0.557
Teacher spread0.058 · 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 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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