Conflict Management among Malay Married Couples: An Analysis on Their Strategies & Tactics
Bibliographic record
Abstract
The purpose of this study is to explore the strategies and tactics used in conflict management and analyze their effectiveness based on quantitative methodology. Probability sampling of 300 respondents in Selangor, Malaysia consisting of Malay married couples were selected using cluster sampling. The findings showed that the strategies were competing, collaborating, compromising, avoiding and accommodating. In average, around 80 percent of Malay married couples chose collaborating strategy whereas competing was less popular. However, the most popular tactic among the respondents is trying to do what is necessary to avoid tension which is under the avoiding strategy. Two-way communication and compromise were seen to be the essence in keeping longevity and success in marriage. The study revealed that there was a change in conflict management among Malay married couples which can be related to the economic development of society, technological advances, political scenarios and the influx of foreign culture. Nonetheless, along with the changes in Malaysia’s economic system, modern Malay couples are more open-minded. Therefore, couples in this study tend to see conflicts as problems that need to be solved, wanting quality decisions that truly resolve the issues. They believe in the power of consensus and in sharing of information and achieving understanding with one another.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".