Trauma-Informed Lawyering: Practicing Emotional Acknowledgment
Bibliographic record
Abstract
This thesis seeks to improve the lives of lawyers and people who use legal systems by exploring complex human needs which can be masked, ignored, and even infringed upon by the legal system. As such, trauma-informed lawyering skills requires a commitment not just to the acquisition of skills, but also to the embodiment and practice of these skills with one’s self as much as with others. This thesis seeks to illustrate how fragmentation and disconnection of the self from one’s emotions impairs, rather than improves the lawyer’s ability to advocate in the best interests of their clients. The true integration and internalization of such trauma-informed and emotional acknowledgment skills is a process that invites transformative change. In order to achieve these outcomes, it is necessary to have a basic understanding of what trauma is, including its history and prevalence. Two types of trauma, intergenerational trauma and indirect trauma, will be discussed. The legal sector’s connection to trauma – the way that trauma is embedded in legal practice - will be explored, with a focus on experiences of emotional suppression and detachment and how these can exacerbate trauma in legal work. The consequences of emotional suppression and detachment are also discussed along with recommendations for individual lawyers to address these. To benefit clients and lawyers, trauma-informed practice must become a mandatory dimension of legal work, rather than an optional skillset. Turning to the organizational level of legal practice, the current efforts of law societies to educate on trauma-informed practice are explored and analyzed. Although progress has been made, including the Université de Sherbrooke’s Phase I Research Report surveying the psychological health determinants of legal professionals in Canada, more needs to be done to prepare lawyers to better respond to the challenges embedded in their work, and the impact on their own internal emotional lives. The thesis concludes with recommendations for organizations such as interdisciplinary research on trauma in law and creating accountability for how legal employers impact or exacerbate lawyers’ mental health. Recommendations for future research on this topic include actively measuring the impacts of legal culture and legal systems as well as adjusting the priorities of such research towards well-being and social improvements rather than focusing solely on economic or productivity levels.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".