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Record W3159762856 · doi:10.1111/fcre.12574

Taking a Shot: Access to Justice, Judging and eCourt

2021· article· en· W3159762856 on OpenAlexaboutno aff
R. James Williams

Bibliographic record

VenueFamily Court Review · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDispute Resolution and Class Actions
Canadian institutionsnot available
Fundersnot available
KeywordsAdversarial systemSettlement (finance)Supreme courtEconomic JusticeDispute resolutionLawNova scotiaPolitical scienceOnline dispute resolutionAlternative dispute resolutionProcess (computing)SociologyComputer science

Abstract

fetched live from OpenAlex

Access to Justice issues have shown us that our traditional adversarial dispute resolution model is slow, costly, divisive and complex ‐ for both self represented litigants and those with lawyers. Addressing these issues through the provision of information to litigants has not been enough. Family Justice reports speak of the need for “culture change” and judicial leadership in affecting change. Judges have created options to the traditional adversarial model with processes such as Settlement Conferences, Binding Settlement Conferences, Informal Trials and Case Management. COVID19 has “forced” courts to embrace Virtual and telephone proceedings. There are now choices in Court based, Judicially‐run dispute resolution processes. Nova Scotia's Supreme Court, Family Division has a process “add” ‐ an eCourt Pilot instituting an electronic, chat‐based Court process that gives litigants and Judges a new “choice” of process.

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)0.014
metaresearch head score (Gemma)0.043
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 score0.053
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0090.008
Scholarly communication0.0150.008
Open science0.0020.008
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0370.003

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 head0.085
GPT teacher head0.337
Teacher spread0.252 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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