Ethics and the Professional Practice of Family Lawyers
Bibliographic record
Abstract
This project involved a survey, interviews and preparation of a report for the Ontario Bar Association. I assisted Toronto lawyer Archana Medhekar in this work and was supervised by Professor Bala of the Law Faculty at Queen’s. This report analyses survey data and interviews done with legal professionals to identify tips for trauma-informed family lawyering, experiences of unprofessional behaviour in family law, and gaps in ethical guidelines for equal access to justice. Following the analysis, recommendations are outlined for lawyers, judges, and the Law Society of Ontario to further build an ethical, trauma-informed practice of family law in Canada. A central theme of the report is that meaningful access to family justice requires building a client-centred practice that reflects an understanding of trauma. People who interact with the family justice system often do so in times of high vulnerability, where manifestations of their traumatic experiences can impact the way they engage with legal professionals. Rather than employing a top-down approach that can alienate clients in their legal journey, trauma-informed family lawyering enables lawyers to collaborate with their clients, empowering them to make informed decisions about what they desire from the justice system.[1] When practicing family law in diverse communities, operating with a holistic, anti-oppressive, and culturally-informed framework is necessary to provide access to justice to clients with intersectional vulnerabilities. Trauma-informed lawyering requires being informed of and building community with extra-legal service providers, such as counsellors and interpreters, for holistic access to justice. Regardless of the outcome of the case at hand, clients should feel that the justice system has provided them with a sense of fairness and closure. References [1] Sarah Katz & Deeya Haldar, “The Pedagogy of Trauma-Informed Lawyering” (2016) 22 Clinical L Rev 359.
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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.026 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".