Pragmatic Hedges in Court Trial: Indonesian Case
Bibliographic record
Abstract
The usage of hedges in trial discourse context is interested to be explored. This paper presents a description of phenomena related to the use of hedges by witnesses and experts in Indonesian court trial. It focuses on the usage of hedges in the form of words, phrases, clauses, and utterances in court trial context. Conversation among participants in court was taken as a corpus of this study. From the corpus, the data were collected in the form of transcription. Three-levels of hedges that classified by Lakoff (1973), Prince, et al. (1982), and Fraser (2010) were used to analyze the data. The analysis was also related to quantity maxim and quality maxim proposed by Grice maxim (1975). This study has shown that the usage of hedges in Indonesian was classified into propositional, approximator, and adjective hedges. They were used to show politeness as well as to hide the real meaning of their utterance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".