Psychological Consequences of Adopting a Therapeutic Lawyering Approach: Pitfalls and Protective Strategies
Bibliographic record
Abstract
The integration of preventive law and therapeutic jurisprudence holds promise for enriching the careers of many practicing lawyers. However, the process of becoming more therapeutic in orientation also involves risk. This Article discusses four potential pitfalls: (1) the process of becoming psychologically-minded and its inherent hazards, including overidentification; (2) the difficulty of balancing neutrality and involvement; (3) the need to identify and manage transference and countertransference; and (4) the risk of secondary trauma. Protective strategies, drawn from the psychotherapeutic and burnout literature, are outlined. This Article stresses the need for lawyers to recognize potential hazards and draw on the experience of other therapeutic professionals as they adopt a more explicitly therapeutic framework, thereby avoiding the pitfalls in favor of the benefits.
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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 | 0.030 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.037 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".