Supporting Accountability in the Field of Mediation
Bibliographic record
Abstract
Abstract Over time, many fields of work—law, medicine, and teaching—matured from informal bodies of knowledge and skills, passed from one practitioner to another, into professions. In the process each began enumerating and affirming their intellectual and practical roots. Each also began testing practitioners to insure comprehension of relevant knowledge and an ability to demonstrate relevant skills. Family mediators in Canada have moved in this direction. In the United States, people who mediate interpersonal disputes also feel pressured to take additional steps to assure the quality of our work. This article summarizes what mediation organizations in the United States have done and could do to vouch for work in our field. But, taking additional steps calls for a clearer understanding of what a mediator should know and what they are expected to do. These two critical pieces of information remain nebulous. So, this article goes beyond describing ways of insuring accountability by also describing a recently completed job analysis, a tool frequently used in other fields of work to describe the skills and knowledge relevant to a particular job.
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 | 0.154 | 0.321 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.025 | 0.039 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".