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Record W2725887378 · doi:10.5553/elr.000078

Administering Justice and Serving the People

2017· article· en· W2725887378 on OpenAlexaffabout
Catherine Piché

Bibliographic record

VenueErasmus Law Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicComparative and International Law Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsEconomic JusticeSociologyPsychologyPolitical scienceCriminologyLaw

Abstract

fetched live from OpenAlex

Administering Justice and Serving the People Canada has a complex system of courts that seek to serve Canadians in view of the traditional objectives of civil justice – principally accessibility, efficiency, fairness, efficacy, proportionality and equality. The Canadian court system is generally considered by its users to work well and to have legitimacy. Yet, researchers have found that ‘there is a tendency for people involved in a civil case to become disillusioned about the ability of the system to effect a fair and timely resolution to a civil justice problem’. This article will discuss the ways in which reforms of procedural law and civil justice have originated and continue to be made throughout Canada, both nationally and provincially, as well as the trends and influences in making these reforms. With hundreds of contemporary procedural reforms having been discussed, proposed and/or completed since the first days of Canadian colonisation on a national basis and in the Canadian provinces and territory, providing a detailed analysis will prove challenging. This article will nonetheless provide a review of civil justice and procedural reform issues in Canada, focusing principally, at the provincial level, on the systems of Ontario and Quebec. Importantly, I will seek to reconcile the increasing willingness to have an economically efficient civil justice and the increased power of judges in managing cases, with our court system’s invasion of ADR and its prioritisation of informal modes of adjudication.

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)0.007
metaresearch head score (Gemma)0.010
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 score0.979
Threshold uncertainty score0.513

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0100.013
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.108
GPT teacher head0.424
Teacher spread0.316 · 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

Citations3
Published2017
Admission routes2
Has abstractyes

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