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Record W4283817272 · doi:10.18192/jpp.v31i1.6441

What Can the Legal Profession Do For Us? Formerly Incarcerated Attorneys and the Practice of Law as a Strengths-Based Endeavour

2022· article· en· W4283817272 on OpenAlexvenueno aff
James M. Binnall

Bibliographic record

VenueJournal of Prisoners on Prisons · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsLawLegal professionPractice of lawPolitical scienceCriminologySociology

Abstract

fetched live from OpenAlex

In recent years, the concept of strengths-based reentry has gained increased attention from scholars and commentators.Proponents of the strengths-based paradigm argue that the formerly incarcerated are far more than a collection of needs and risks.Rather, we bring unique skills to the reentry process that can be utilized to engage in generative activities that serve to diminish the stigma of a criminal history and to promote post-release success.Drawing on my own journey from prison to practicing attorney, this article contemplates the legal profession as one such generative activity.By serving clients at risk of criminal justice system involvement and organizing to promote experiential diversity at law schools and in the bar, many formerly incarcerated attorneys are engaged, often subconsciously, in ongoing stigma/shame management at the micro and macro levels respectively.For these reasons, this paper contends that the legal profession ought to be considered a viable, realistic option for formerly incarcerated students, as they possess the empathy to excel as attorneys and to use the law as a means of transforming their own self concept.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.392
Teacher spread0.367 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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