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Record W2775761272 · doi:10.22329/wyaj.v34i1.5008

ONLINE DISPUTE RESOLUTION AND JUSTICE SYSTEM INTEGRATION: BRITISH COLUMBIA’S CIVIL RESOLUTION TRIBUNAL

2017· article· en· W2775761272 on OpenAlexvenueaboutno aff
Shannon Salter

Bibliographic record

VenueWindsor Yearbook of Access to Justice · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDispute Resolution and Class Actions
Canadian institutionsnot available
Fundersnot available
KeywordsTribunalOnline dispute resolutionEconomic JusticeDispute resolutionPropositionTransformational leadershipPolitical scienceResolution (logic)LawAlternative dispute resolutionMediationComputer sciencePublic relationsArtificial intelligence

Abstract

fetched live from OpenAlex

This article undertakes a brief comparison of private and public online dispute resolution [ODR] systems before providing an overview of the Civil Resolution Tribunal [CRT], Canada’s first online tribunal, and its ODR processes. The article discusses why the CRT has come to be, how it has been implemented, as well as its implications for civil justice reform more broadly. A main proposition is that the transformational potential of ODR will only be realized when ODR is fully integrated with public justice processes. This proposition is not without its difficulties, as the CRT’s experience illustrates. To this end, the article also provides an introduction to some of the opportunities and challenges offered by an integrated ODR system like the CRT as well as some of the steps the CRT has taken to meet these demands as transparently and collaboratively as possible.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.110
Threshold uncertainty score0.797

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0350.008
Scholarly communication0.0210.004
Open science0.0030.007
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0180.002

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 head0.037
GPT teacher head0.282
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2017
Admission routes2
Has abstractyes

Explore more

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