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Record W3125187840

THE EVOLUTION OF SMALL CLAIMS COURT: RISING MONETARY LIMITS AND USE OF LEGAL REPRESENTATION

2015· article· fr· W3125187840 on OpenAlexaffabout
Shelley McGill

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsRepresentation (politics)Political scienceLaw and economicsLawEconomicsPolitics
DOInot available

Abstract

fetched live from OpenAlex

Do higher small claims court monetary limits expand access to “simplified” justice or do they erode the character of the “People’s Court”? This paper reports the initial findings of a study of claims filed in the Toronto Small Claims Court in the two years before and after Ontario raised the Court’s monetary limit from $10,000 to $25,000. Claim values and distribution across the value range are significantly changed following the monetary limit increase while claim volume remains at pre-increase levels. Predictably, proportionate use of legal representation by plaintiffs and defendants also increases after the limit increase.\n\n \n\nLes limites pécuniaires plus élevées qui s’appliquent à la compétence de la Cour des petites créances ont-elles pour effet d’élargir l’accès à la justice « simplifiée » ou plutôt d’affaiblir le caractère populaire de cette « cour du peuple »? Ce document fait état des premiers résultats d’une étude des réclamations déposées à la Cour des petites créances de Toronto au cours des deux années qui ont précédé et suivi la date à laquelle l’Ontario a fait passer la limite pécuniaire de la compétence de la Cour de 10 000 $ à 25 000 $. Les valeurs des réclamations et leur répartition dans la fourchette de valeurs évoluent sensiblement après l’augmentation des limites pécuniaires, tandis que le volume des réclamations demeure semblable à celui qu’il était avant cette hausse. L’utilisation proportionnelle de représentants juridiques par les demandeurs et les défendeurs augmente également après l’accroissement des limites, ce qui était prévisible.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.653

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.328
GPT teacher head0.442
Teacher spread0.114 · 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 designObservational
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

Citations0
Published2015
Admission routes2
Has abstractyes

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