Bibliographic record
Abstract
Mediators have shown great ambivalence about emotions, with practice lurching between intrusive fascination (“How does that make you feel?”) and denial (one prominent pioneer describing emotional information as “not useful”). Emotions are also physical, and mediation has also proved itself less than comfortable with the physical dimensions of human interaction. The classic model involves sitting, talking and thinking – “mediating from the neck up.” And yet all know the visceral effect of conflict. Bodies matter: “Our evaluations of the world … rely on a seamless calibration of feelings and thoughts. Body and mind are equally implicated” The chapter explores three key ideas: 1) The relationship between cognition and emotion in perception 2) The importance of a range of emotions, starting with anger, in contributing to conflict 3) The potential for emotional self-regulation to be harnessed and supported by mediators It goes on to set out the “emotion grid”, a simple heuristic with twin poles of volume and intensity, designed to support conflict resolution practitioners in: 1) developing cultural fluency 2) plotting the flow of emotions over time 3) developing mediator practice through self-reflection 4) helping clients build their capacity for emotional self-regulation. This chapter emerged from “Dancing at the Crossroads”, an innovative conference led by Michelle LeBaron and Margie Gillis (one of Canada’s best known contemporary dancers). Conflict resolution practitioners and artists from across the world gathered in Switzerland in the summer of 2013 to engage in an imaginative experiment in creativity and physicality, culminating in the publication of “The Choreography of Resolution: Conflict, Movement, and Neuroscience.” The “emotion grid” was one of the products of that week.
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 | 0.044 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.029 | 0.040 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".