Investigating Leech’s Politeness Principle in Conversational Verses in Three Surahs from The Holy Quran
Bibliographic record
Abstract
This study has investigated three Surahs from the Holy Quran, translated into English by Arberry (1955), in terms of the Politeness Principle proposed by Leech (1983). The study aimed to investigate the kinds of politeness maxims employed by the characters in the three Surahs in question. The intentions of the speakers in observing or flouting each of Leech’s politeness maxims have been categorized, including six maxims of the Politeness Principle: tact maxim, generosity maxim, approbation maxim, modesty maxim, agreement maxim, and sympathy maxim. The research had applied a mixed-methods approach in analyzing the obtained data. The data consisted of the utterances uttered by the characters in the three Surahs. After collecting the data, the data classified into six maxims of the Politeness Principle. Then, several conclusions had been drawn based on the research findings. The results of the study showed that the characters used six maxims: tact maxim, generosity maxim, approbation maxim, modesty maxim, agreement maxim, and sympathy maxim. Finally, the findings indicated that three maxims had been flouted by the characters: tact maxim, generosity maxim, and agreement maxim.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".