Implementation of Partnership Principles in Cross-Cultural Communications Amongst Malay, Akit, and Chinese Ethnics
Bibliographic record
Abstract
A Cross-cultural communication occurs in multicultural and multilingual societies. Selatpanjang is a multicultural and multilingual city with "native" ethnic Malays, Chinese, and Akit people. This research aims to describe the implementation of partnership principles in cross-cultural communication amongst Malay, Akit, and Chinese ethnics in Selatpanjang. This study uses a descriptive method. Data were collected through observations and interviews. The steps taken in analyzing the data are data reduction, data categorization, data synthesis, and formulation of research findings. The research findings revealed that the implementation of partnership principles in cross-cultural communication amongst Malay, Akit, and Chinese ethnics is as follows: 14.2% of respondents from the Malays implementing the partnership principles belong to the "very good" category. In addition, 43.2% of them belong to the "good" category. 42.5% of them belong to the "fairly good" category. None of them fall into the "poor" category; 58.7% of respondents thought the Akit ethnic group fell into the "very good" category, 31.2% fell into the "good" category, 10% fell into the "fairly good" category, and none fell into the "poor" category. Regarding Chinese ethnics, 38% of them belong to the "very good" category, 22.7% belong to the "good" category, 18.25% belong to the "fairly good" category, and none belong to the "poor" category. In addition, the findings revealed that the implementation of partnership principles cannot be separated from the language characteristics of each ethnic group. Akit people tend to convey the right amount of message and the right information, focus on the topic of conversation, and speak concisely "according to" their rigid, closed, and flat character. Ethnic Malays commit several violations of the maxim of the partnership principle, influenced by their humorous, adaptable, and self-limiting character. Chinese tend to be serious and focused, but they speak longer to convince interlocutors.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".