The mediator as nonviolent advocate: Revisiting the question of mediator neutrality
Bibliographic record
Abstract
Abstract This article presents the argument that the roles of interpersonal mediator and nonviolent advocate/activist are best carried out when they are each understood as being part of a larger framework of conflict resolution that makes room for them both. It draws on the relevant literature to date that supports the thesis that the skills and energies of both nonviolent advocacy and mediation should come into play in the actual practice of either role. To this end, it challenges the notion of neutrality as a guiding concept in the practice of mediation, suggesting instead that mediators, like nonviolent advocates, should determine the degree to which they intervene or influence the content of a session by the communicative behaviors of those in conflict. Finally, a visual model for illustrating the concrete ways in which the skills, conceptual resources, and energies of nonviolent advocacy might come into play in the practice and training of mediation is presented. The implications of this article are that we, as Western practitioners of mediation, must fundamentally rethink the way we define, carry out, and teach our role by looking, at least in part, to the assumptions and practice of nonviolent advocacy/activism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.075 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| 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".