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Record W3175488539 · doi:10.22329/wyaj.v37i1.7192

Civil Revolution: User Experiences with British Columbia’s Online Court

2022· article· en· W3175488539 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueWindsor Yearbook of Access to Justice · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDispute Resolution and Class Actions
Canadian institutionsUniversité de MontréalUniversité LavalUniversity of WindsorUniversité du Québec à MontréalThompson Rivers University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTribunalEconomic JusticeOnline dispute resolutionExploratory researchDispute resolutionPolitical scienceAlternative dispute resolutionResolution (logic)LawPublic administrationLibrary scienceSociologyComputer scienceArtificial intelligenceSocial science

Abstract

fetched live from OpenAlex

British Columbia’s new Civil Resolution Tribunal [CRT] is a primarily online dispute resolution system that has attracted international attention for its innovative approach. But so far there has been little independent research on the effectiveness of the CRT and similar online dispute resolution initiatives in providing access to justice. In a qualitative and exploratory study, we surveyed 49 British Columbians who had used the CRT about their experience with the process. Overall, the results suggest that the CRT has improved access to justice, but the survey answers also identified problems and concerns, for which we suggest potential solutions.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.024
GPT teacher head0.253
Teacher spread0.229 · 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