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Record W2530013195 · doi:10.1177/2322005816662375

Understanding University Pro Bono

2016· article· en· W2530013195 on OpenAlexaboutno aff
Balawyn Jones, William Lee, R. G. Morrison

Bibliographic record

VenueAsian Journal of Legal Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Political scienceSustainabilitySubject (documents)Public relationsBusinessPublic administrationLibrary scienceGeography

Abstract

fetched live from OpenAlex

This article develops an approach to establishing university pro bono (UPB) programmes in developing legal jurisdictions. First, through comparative research and surveying of pro bono programmes in the United Kingdom, Australia and Canada, factors which affect the development of UPB programmes are identified. Second, the authors explore the applicability of different pro bono programme structures and project types, given available resources, supervisory capacity and student participation in the context of developing legal jurisdictions. It was found that limited financial resources or internal or external managerial support does not in itself prohibit the establishment, development or sustainability of UPB programmes. Instead, it simply influences the choice of programme structure and types of projects which can be successfully undertaken. In conclusion, the article advocates for a context appropriate ‘adaptation’ of UPB programme structures and projects as opposed to direct transplantation. Pro bono culture, perceptions of student capability and local context are all key factors which will affect the development of UPB and the types of projects that can be undertaken. Subject to these factors, it was found that legal research and community legal education projects are generally the most appropriate types of projects to run where resources are limited.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.125
GPT teacher head0.368
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2016
Admission routes1
Has abstractyes

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