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Record W2789004301 · doi:10.22329/wyaj.v34i2.5020

REDUCING THE “JUSTICE GAP” THROUGH ACCESS TO LEGAL INFORMATION: ESTABLISHING ACCESS TO JUSTICE ENTRY POINTS AT PUBLIC LIBRARIES

2018· article· en· W2789004301 on OpenAlexafffundvenue
Beth Bilson, Brea Lowenberger, Graham Sharp

Bibliographic record

VenueWindsor Yearbook of Access to Justice · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsEconomic JusticePublic accessContext (archaeology)Political scienceIntermediaryPublic relationsInformation accessScope (computer science)LawBusinessPublic administrationSociologyInternet privacyLibrary scienceComputer science

Abstract

fetched live from OpenAlex

Among the strategies to improve public access to justice, increasing the accessibility and comprehensibility of legal information must be ranked as important. In this paper, the authors explore how libraries and librarians might play a role in providing the public with access and guidance to legal information. These issues are considered primarily in the context of two scenarios: that of the self-represented litigant, and that of a party to a limited scope retainer. The authors consider in particular how public libraries as a public space and public librarians as trusted intermediaries might support the objective of greater access. The possible roles of law society/courthouse and academic libraries in training and collection development are also considered. The distinction between providing access to legal information and giving legal advice is discussed briefly, and the authors suggest some possible ways of clarifying this distinction while pursuing the goal of expanding public access to legal information.

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)0.002
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0030.000
Scholarly communication0.0120.048
Open science0.0040.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.131
GPT teacher head0.407
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2018
Admission routes3
Has abstractyes

Explore more

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