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Record W4313462896 · doi:10.5430/wjel.v13n1p337

A Comparative Analysis of Politeness Strategies in the Animated Cartoon Angelo Rules and Its Dubbed Arabic Version

2023· article· en· W4313462896 on OpenAlexvenueno aff
Majedah A. Alaiyed

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsPolitenessArabicLinguisticsContrastive analysisPsychologyPoliteness theoryPhilosophy

Abstract

fetched live from OpenAlex

The aim of this paper is to compare the politeness strategies in the English version of the animated cartoon Angelo Rules with those in the dubbed Arabic version, focusing on whether the different cultures have an effect on the politeness strategies used. The data for the analysis came from four episodes in English and four episodes in Arabic that were identical to the English episodes, and the analysis was based on Brown and Levinson’s framework. The study examined the use of positive politeness, negative politeness and bald on-record strategies by the characters. The findings revealed the existence of positive politeness, negative politeness and bald on-record strategies, which were found in nearly all the English and Arabic episodes analysed. Positive politeness (in particular, exaggerating interest and including both the speaker and hearer in the activity) and bald on-record strategies were the most commonly used in the selected data. Furthermore, no significant difference in politeness strategies used was found between the four English episodes and the same episodes dubbed into Arabic.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.025
GPT teacher head0.321
Teacher spread0.296 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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