Some Reflections on the Role of Power in Track II Mediation
Bibliographic record
Abstract
Power is a central feature of both Track I (formal) and Track II (informal) mediation. Power intersects the mediation process at every stage and is deeply embedded in the process, its design and structure, as well as who facilitates it. This paper addresses the question of how to manage these and other power dynamics and what can be done to alter them. Four key insights are presented based on the author’s personal experience undertaking peacemaking and mediation in Canada and overseas over the last twenty years. The four insights are that: (1) Convening power is shaped by the type of process and who is running it; (2) The mediator has procedural power but exercising it might create a reputational cost; (3) Power imbalances are likely to occur and the mediator needs to make a conscious effort to address them; (4) Power, which is often deeply embedded in the social institutions where the conflict is occurring, can be used for either constructive (peaceful) or destructive (violent) purposes and that decision is influenced by leaders from different sectors (political, military, etc.). Based on these four key insights, several recommendations for mediation and peacemaking actors to address power dynamics are developed.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| 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".