Politeness Strategies in Directive Speech Acts in Local Indonesian Parliament Assembly Proceedings
Bibliographic record
Abstract
This study reports on politeness in directive speech acts appearing within the proceedings of the local parliament for Sukoharjo, Indonesia. The aim is to explain the politeness strategies used to convey intended persuasive forces during parliamentary discourses. Drawing upon the pragmatic qualitative approach, this study examined 18 parliamentarians and data on their previous utterances’ form, function, meaning, and context in the proceedings. Using data collected through observation, records, and documentation, it looks at how the politicians acted. The results show that directive acts represent the main performance, with 154 tokens of illocution and 44 directive speech acts for politeness. Politeness strategies to perform directive speech acts are colored with on record, positive politeness, and aversion-to-acting negative politeness. The characters for positive politeness include inviting-gentle-direct, repressing-gentle-direct, suggesting-gentle-indirect, repressing-gentle-indirect, gentle-indirect, and respecting direct. This study implies pragmatic analysis in a different setting where an emphasized degree of formality is required. Suggestions are made to compare or contrast with utterances in less formal interactions, such as in the negotiations between a buyer and seller, and in religious circumstances like sermons in a mosque, church, or colloquial proceedings.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".