Developing Students’ Intelligent Character through Linguistic Politeness: The Case of English as a Foreign Language for Indonesian Students
Bibliographic record
Abstract
<p>English is a foreign language that must be taught at school, particularly in secondary school. Based on a preliminary observation of several secondary schools in Banjarmasin, it appears that the English taught focuses most on concepts or language formulas. Most of the students who interact in English during the learning process do not use expressions that contain linguistic politeness, as is required. The learning of linguistic politeness is not emphasized, while it is an effort to develop students’ intelligent characters. This study primarily focuses on the investigation of teachers’ linguistic politeness while interacting with the students, students’ linguistic politeness, while interacting with the teachers, the students’ linguistic politeness while interacting with their peers during the learning process in the classroom, how the teacher forms the students’ linguistic politeness in the classroom, and how the linguistic politeness can develop students’ intelligent characters. This study is one of classroom action research. Two cycles, in which each cycle consists of two meetings, are employed. After linguistic politeness is taught in four meetings through students’ wheel and role play, it can be stated that during the English learning process in the classroom, the students have the opportunity to speak and practice linguistic politeness in English while interacting with their teachers and or other students. The forming of linguistic politeness in English can develop the students’ intelligent characters from the beginning to the end of the learning activities. The students also become accustomed to employing polite vocabulary or expression in English that can improve their spiritual and emotional development, the aim of which is to lead to intelligence, primarily emotional intelligence.</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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".