Mediation Game When the Conflict Can Be Fun to Learn—A Legal Skill Learning Tool: The Integration of Knowledge Management, Learning Theory and Serious Game Concept
Bibliographic record
Abstract
Legal justice in Thailand has been shifted to restorative justice for reasons. But Thai law schools have not been changed to promote lawyering skill learning opportunities due to various obstacles and limitations caused by existing legal educational policies, law curriculum’s structure, knowledgeable instructors, and learners’ characteristics. As a result, most graduate law students have been faced with difficulties as regards not only their mediation skill capacity but also other legal skills. To solve the problem beyond the single loop learning, the study proposed the integration of knowledge management approaches, an appropriate learning theory, and serious game concept to initiate an alternative learning tool to enhance mediation skill learning. The study demonstrated the two crucial stages of game designing and experimentation to verify the potentiality of games in enhancing the knowledge and learning engagement. The outcome of such game designing and experimentation can provided both a satisfied interactive learning tool and a linkage for the flow of advocacy skills knowledge from the community of experts to law students who, sooner or later, will be their competent team workers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".