Common Language, Different Meaning: What Meidators Mean When They Talk About Their Work
Bibliographic record
Abstract
Abstract Mediators, for the most part, descrive their work as “facilitation” but what they actually mean varies considerably. Based on an exploratory study with nealy 90 mediators in Canada (all of whom are also mediation trainers), the author describes the great diversity among mediators’understanding of commonly-used terms like facilitation, transformative, settlement, and humansitic. She also reports on how such factors as context, gender, and number of years mediating affect mediator perceptions of what they do. In addition, the author shows how perceptions affect the overall philosophy and goal of hte meidation practitioner: One implication of this research is that we can no longer presume to know what people men by “mediation,” nor can we assume mediators are like-minded in how they understand their work. Thus, practitionsner, scholars and policymakers are encouraged to be purposefully clear when describing and writting about the practice of mediation.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| 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".