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Record W147977947

Painful Injustices: Encountering Social Suffering in Clinical Legal Education

2012· article· en· W147977947 on OpenAlexaff
Sarah Bühler

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsInjusticePoliticsSociologyLegal educationPsychologyReification (Marxism)Social psychologyLawPolitical science
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.438
Teacher spread0.395 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2012
Admission routes1
Has abstractyes

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