Pragmatic Variations in Giving Advice in L2 by Malaysian Postgraduate Students: The Situational Effects
Bibliographic record
Abstract
The present study attempted to describe the giving advice strategies utilized by Malaysian postgraduate students in confronting different situations. In addition, it examined the effects of the situational factors of social distance, power, and imposition on the students’ choice of giving advice strategies. Another objective was to categorize the challenges students face in the production of giving advice in English. One hundred and ten Malaysian postgraduate students majoring in different fields voluntarily participated in this study. A Written Discourse Completion Task Questionnaire and semi-structured interviews were utilized for data collection procedure. The results of the questionnaire illustrated that the respondents tended to use more direct strategies to give advice. The first most frequently strategy used by the respondents was obligation strategy, 53.38%., mood derivable strategy with 30.08% as the second most frequently used strategy and performative as the third one, while no respondent used the hedged performative and want statement strategies in any of the situations. The respondents also opted out the same strategies almost with similar frequency in most of the situations. It means that the choice of strategies was not different in terms of the three situational variables of power, distance and imposition. In addition, the results of interviews showed that the challenges they face in the production of advice giving include expression, structure, culture, social values, first language, gender, age and educational background of the interlocutors. This study has some implications for second language acquisition research and intercultural communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".