Bibliographic record
Abstract
Abstract Within the context of family law mediation, the author explores the issue of giving advice and performing related interventions that mask a similar intention. It does so by examining some codes of conduct and professional standards from Australia, Canada, and the United States and also by drawing on recent literature concerning mediator intervention, particularly on the subjects of impartiality and neutrality, reframing, mediator pressure, ethics, and the concept of the mediator as “folkloric trickster.” The author concludes that mediator intervention (such as creating doubt, reframing, and applying pressure to respond to a mediators concern) and selective facilitation obviously do not fit the definition of giving advice in the narrow sense by recommending a specific course of action. Nevertheless, they also are not simply an intervention of process (rather than one of content), for they are intended to redirect a party's attention to a hitherto unthought of or unarticulated substantive possibility. Thus the mediator may be seen, in broad terms, as an advocate for fair and effective use of the process of mediation to safeguard or enhance party self determination. To forward this aim, he or she may use an intervention intended to impart advice while still respecting party autonomy.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.295 | 0.190 |
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".