Understanding the Legal Client’s Best Interests: Lessons from Therapeutic Jurisprudence and Comprehensive Justice
Bibliographic record
Abstract
The Canadian Bar Association’s Code of Professional Conduct and the American Bar Association’s Model Rules of Professional Conduct provide frequent direction to promote the client’s best interests. However, neither Code defines “best interests” or sets out a process to determine them. Surprisingly little has been written in an attempt to gain a more complete understanding of the client’s “best interests.” What can be seen in traditional legal practice is a predominantly adversarial understanding that is premised upon zealous representation limited to the scope of the legal case. However, therapeutic jurisprudence urges lawyers to deal with cases in ways that promote the therapeutic benefits of the law and reduce the anti-therapeutic consequences. The comprehensive law and comprehensive justice literature embrace an expanded definition of justice and “best interests” that go beyond simple legal interests. The crux of the problem is this: what do lawyers practicing in accordance with the prescriptions of therapeutic jurisprudence, comprehensive law, or comprehensive justice do when a client’s actual best interests are at odds with what traditional adversarial legal representation assumes? In following the requirements of the code of conduct, and advancing the legal case, the adversarial lawyer may pursue a legal-ethical course of action to the detriment of the client’s actual best interests. Alternatively, the lawyer who promotes the client’s actual best interests may pursue a socio-ethical course of action to the detriment of the client’s legal best interests. This Article argues that while the client’s legal best interests are still relevant to decision-making, the client’s legal best interests are at most a subset of the client’s actual best interests. To properly determine a client’s actual best interests and to provide appropriate legal advice, lawyers may need to refer their clients to the expertise of other professionals during case planning. This will be necessary in all cases where such advice is required to enable the client to properly identify his or her actual best interests and to instruct the lawyer accordingly. The goal must be for lawyers and their clients to develop a more complete understanding of the client’s actual best interests, one that is cognizant of factors beyond a narrow focus on the client’s legal case. Finally, to facilitate this broader understanding, this Article suggests that certain provisions in the Codes need to be amended or more robustly interpreted to promote the client’s actual best interests; and, some initial suggestions for amendment are set out.
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 | 0.038 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.169 |
| Scholarly communication | 0.025 | 0.039 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.022 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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".