Bibliographic record
Abstract
Many law students experience strong and sometimes difficult emotions during their time in clinical lawprograms: sadness at clients'stories of trauma, excitement about a victory in court, or anger at the injustices faced by clients. In this article, I focus on the emotion of "moral anger,"or "moral outrage" experienced by lawyers and students in clinicalcontexts, and consider how educators and students might address manifestations of moral anger in clinical law contexts in ways that ignite a critical and social-justice oriented approach to legal practice. By drawing on theoretical insights from the emerging field of critical emotion studies, I argue that a critical analysis of the role of moral anger in clinical legal education reveals its potential as an agent of transformation, but also signals a need for clinical educators to be wary of an uncritical understanding of this strong emotion. Drawing on the work of Michalinos Zembylas, Sara Ahmed, and others, Ipropose that clinicallaw students and teachers should seek to engage in critical "readings" • of moral anger-interpretations that acknowledge the role of strong emotions in legal practice, and then interrogate the meaning of these feelings in light of community context, power relations, and history. Such an approach, I argue, can literally "move" us into deeper understandings and potentially more meaningful and collaborative social-justice oriented practices.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".