Teaching Conflict Resolution through General Education at University: Preparing Students to Prevent or Resolve Conflicts in a Pluralistic Society
Bibliographic record
Abstract
This research stems from an appreciation of ethnic diversity among university students, and the awareness thatdifferences can result in verbal or physical conflicts. University lecturers have an important role to play inhelping prevent and resolve student conflicts. Hence, they are required to create a climate conducive to teachingand learning, where what is taught has meaning in the student’s life. In the present research, we focused on“Civic Education”- a course meant to create awareness among students on the importance of nationalism,national identity and peaciful living within society from the Indonesian perspective. To establish how universityeducation can help to develop students into good conflict managers and to understand how they can solvedisputes arising from the differences in opinion, this research was carried out in two stages: the first stageinvolved preliminary information gathering with a view to designing a model for teaching conflict resolution.While the second stage was the implementation phase of the conflict resolution teaching model. The modelentailed discussions, practical problem solving skills, and role-playing. The activities carried out comprised ofreviewing the literature and course syllabus, collecting information, administering questionnaires, carrying outinterviews and finally formulating the model. The interviews were conducted at the campuses of universitiesprone to conflicts. The researchers employed a qualitative research approach, with the help of a case studymethod. Data collection techniques comprised of questionnaires, interviews, observations, and documentanalysis. The findings revealed that: 1) Lecturers lack knowledge and skills concerning conflict resolution andprevention among students; and 2) It was established that the implementation of the conflict resolution modelhas not been effective among students. Because they do not understand the stages involved in a peaceful conflictresolution or prevention process.
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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 | 0.003 | 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 | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".