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Record W4382358225 · doi:10.4236/jss.2023.113012

A Politeness Principle-Based Study on Subtitle Translation for Business Greeting Videos

2023· article· en· W4382358225 on OpenAlexaboutno aff
Huang Huang

Bibliographic record

VenueOpen Journal of Social Sciences · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsSubtitlePolitenessNegotiationPoliteness maximsComputer sciencePublic relationsLinguisticsAdvertisingPolitical scienceBusinessLaw

Abstract

fetched live from OpenAlex

With the continuous development of the global economic integration process, economic cooperation and exchanges worldwide are also increasing. People need help to meet and negotiate with their trading partners in person. Greeting videos have become a cheap and effective communication medium in place of in-person meetings. Following the principle of politeness helps to promote and develop relationships with customers and plays an active role in facilitating new business. This paper discussed the politeness principle of Western and China, which later on was used to analyze the source text selected in the opening ceremony of Sino Canada Health Engineering Institute. Combining the characteristic of subtitle translation, this paper then gives an opinion on the translation of the chosen source text in the hope of correctly translating the business greeting videos and matching the videos with appropriate subtitles while illustrating the importance of the politeness principle.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.344
GPT teacher head0.441
Teacher spread0.096 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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