Triaging and Mediating to Meet the Needs of Families Under The Family Dispute Resolution (Pilot Project) Act of Manitoba
Bibliographic record
Abstract
The Family Dispute Resolution (Pilot Project) Act of Manitoba (“FDRA”) creates a three-year pilot project which will mandate the resolution of certain family disputes outside of the courts. Under the FDRA, “resolution officers” will be responsible for triaging families into these alternative resources. Currently, without supplementary regulations, the FDRA provides insufficient guidance to resolution officers to enable them to conduct this triaging role effectively. This is problematic as triaging is the first major step in the FDRA process and will set the course for the parties’ entire dispute resolution experience under the new scheme. Given the importance of this step, and the likelihood that mediation will be one of the primary processes used to resolve disputes under the FDRA, I have attempted to create enhanced guidelines to help resolution officers match parties to the optimal type of mediation to fit their particular needs. These guidelines, which can hopefully help to inform the future drafting of regulations to the FDRA, were informed by both the mediation literature and the results of qualitative interviews which I conducted with some of Manitoba’s most knowledgeable family mediators. Ultimately, I outline several factors which can impact the resolution of family disputes through mediation, and which must therefore inform the triaging decisions of resolution officers. I also argue that resolution officers should be required to receive specific professional mediation designations, and that to facilitate the most successful implementation of the FDRA, the government should not only take the insights from my research into consideration but should also commit to further consultations with our province’s family mediators and other ADR professionals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".