Bibliographic record
Abstract
<p>Learning a foreign language is not just focused on learning the language grammatically, but also pragmatically that is spoken in accordance with the proper context. Mistakes in particular speech act speech act can have an impact on the problem of refusing to face up to conflict. This paper wants to see how the strategy of rejecting speech acts committed by Indonesian students who study French. Strategies analyzed by the selection vocabulary, effectiveness of sentences, sentence structure and politeness. Respondents are 30 students majoring in French at the University of Medan were selected based on purposive sampling technique. Data were collected using Discourse Completion Test (DCT). The results show that in the choice of vocabulary, many respondents use the verb is not appropriate to express rejection. Respondents were also frequent repetition of words in a sentence that makes the sentence to be long and rambling. In refusing, respondents are very polite, especially in the interaction between faculty and students. However, politeness is only indicated with concomitant use of words such as <em>madame (madame),</em> <em>monsieur (sir)</em> and <em>excuse the expression (z) -moi (pardon me), je suis désolé (e)</em> (I regret). Whereas for the polite form sentences in French can be used with <em>conditionel </em>mode, and other strategies such as the use of the phrase <em>impersonnel</em>, neutral pronoun use on, or the passive sentence. These strategies do not look at the answers of the respondents.</p>
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".