Advocacy in Non-Adversarial Family Law: A Recommendation for Revision to the Model Code
Bibliographic record
Abstract
Family law is evolving towards non-adversarial dispute resolution processes. As a result, some family lawyers are representing clients who are trying to reach settlements that recognize their interests, instead of just pursuing their legal rights. By responding to the full spectrum of client needs, lawyers are required to behave differently than they do when they are representing a client in a traditional civil litigation file. They consider the emotional and financial consequences of relationship breakdown – things that are not typically within the purview of the family law lawyer. They objectively reality check with their client, and they approach interest-based negotiations in a client-centric way. These lawyers view their role as that of a non-adversarial advocate, and their client as a whole person with interests that are not just legal. This paper draws on an empirical study involving focus groups with family law lawyers, to argue that the Federation of Law Societies of Canada, Model Code of Professional Conduct, needs to be updated to incorporate non-adversarial advocacy. The lawyers in the study viewed non-adversarial advocacy as being responsive to client needs, and in the interest of the client's children. This paper draws from the study to establish what constitutes non-adversarial advocacy and then it presents a proposal for revising Rule 5 (Advocacy) of the Model Code.
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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 | 0.100 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.037 | 0.044 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".