A Comparative Analysis of Politeness Strategies in the Animated Cartoon Angelo Rules and Its Dubbed Arabic Version
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".