NEGATIVE POLITENESS STRATEGIES IN BATAM COMPANIES' ENGLISH BUSINESS LETTER
Bibliographic record
Abstract
This research aimed to the being pessimistic politeness strategy in the text of business letter from several Companies in Batam, Riau Island, Indonesia. This study includes multiple companies, with three companies chosen at random. Brown and Levinson (1987) define negative politeness methods as displaying restraint, formality, and distance. A descriptive qualitative method was used in analyzing the data because it would be explained by words, phrases, and sentences. The researcher employed the Sudaryanto (2015) observational method to obtain data. Brown and Levinson identified five negative politeness methods; however, in this study, the researchers focused solely on the pessimistic strategy. Pragmatics identity method was applied in analyzing the data. it was found that there were nine strategies of being pessimist politeness in the text of business letters. There are 2 data of being pessimistic in Letter 1 by PT. Vancouver Manufacturing Company, 4 data of being pessimistic in the Letter 2 by ABC Software Company, and 3 data of being pessimistic in Letter 3 by Mass Airlines Company.
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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 | 0.002 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".