Painful Injustices: Encountering Social Suffering in Clinical Legal Education
Bibliographic record
Abstract
In this article, I examine and problematize clinical law students’ encounters with traumatic stories and expressions of suffering of some clients. I argue that without critical reflection, these encounters can function to produce and reinforce dominant understandings of suffering as a non-legal, private emotional or psychological attribute of clients, a matter to be referred to other professionals, ignored, or otherwise “managed” by the lawyer. This type of “reading” of suffering, I argue, can serve to reinforce acontextual, uncritical legal practice. Furthermore, I argue that in clinical law contexts this reification of notions of professional identity and role is often problematically compounded with the reproduction of dominant images of poor clients as victims, or as helpless, or as responsible for their suffering. By drawing on the eclectic and emerging body of literature on “social suffering,” as well as the critical feminist and post-colonial theoretical literature on emotions, suffering and “embodied encounters,” I highlight the importance of paying attention to encounters with human suffering in clinical legal education. I describe aspects of a critical “pedagogy of suffering” in clinical law contexts, a pedagogy that views human suffering as a signifier of larger political and systemic injustice, and one which encourages critical, attentive and politicized “witnessing” and responses to suffering by lawyers and law students.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.060 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".