The work of conflict mediation: Actors, vectors, and communicative relationality
Bibliographic record
Abstract
Mediation is a widely used form of third-party conflict management for which research has primarily focused on the role of mediators. But how are the relations between disputing parties constituted in communication involving written texts, such as official letters or medical reports, during mediation sessions? To gain deeper insight into the communicative dynamics through which third-party disputes are created, sustained, and resolved, this article proposes a new theoretical perspective on mediation that illuminates how human beings and written texts can act as vectors for each other, i.e., how they can make important differences in mediation sessions because they carry or convey what someone or something else is saying, doing, thinking, or feeling and, thus, contribute to composing the nature of disputants' relations. The value of this vectorial perspective on mediation is subsequently demonstrated through an inductive analysis of video-recorded sessions that took place at an administrative tribunal in Canada. By showing how texts (or their absence) can act as (1) conjunctive vectors that contribute to highlighting disputants' compatibilities and help them find common ground, or (2) disjunctive vectors that contribute to highlighting their incompatibilities and obstruct their dispute resolution, this article advances the academic and professional literature on the role of communication in conflict mediation work, and reveals significant implications for the study and practice of conflict management in organizations as well as scholarship on relational ontologies.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.082 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".