Bibliographic record
Abstract
Purpose Mediators' impartiality and empathy are two classical factors in the parties' trust in mediators. However, mediators are often torn between being impartial and being empathetic. The aim of this paper is to explore this empirically. Design/methodology/approach This study empirically tests the strategic use of caucus to improve the interaction between impartiality and empathy by splitting them into two phases: impartiality in joint sessions and empathy in caucus. Findings The strategy did create significant synergy between impartiality and empathy with the main impact of reducing the time needed to reach an agreement. Research limitations/implications All research data come from workplace mediation and from the same organization. Although it can be reasonably postulated that the results can be generalized to other mediation settings, this remains to be proven. Practical implications When mediators use the trust caucus strategy, impartiality and empathy work better together and parties put more weight on empathy than on impartiality. While the use of the trust caucus does not increase the likelihood of reaching agreement, it does significantly decrease the time needed to conclude an agreement. Originality/value The study uses a quasi‐experimental design to test its hypothesis. Furthermore, the study uses real mediation cases.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".