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Record W7128209445 · doi:10.3138/ccar.v13i2.255

“Cost-Shifting” and Access to Justice: A Quantitative Review of Certification Motion Cost Awards in Ontario

2018· article· en· W7128209445 on OpenAlexaboutno aff
Brandon Pasternak

Bibliographic record

VenueCanadian Class Action Review · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDispute Resolution and Class Actions
Canadian institutionsnot available
Fundersnot available
KeywordsPlaintiffCertificationContext (archaeology)CommissionClass (philosophy)Class action

Abstract

fetched live from OpenAlex

Abstract: In the twenty-five years since Ontario’s Class Proceedings Act, 1992 has come into force, a number of concerns have been expressed by judges, practitioners, and academics regarding the success of Ontario’s “cost-shifting” regime. In particular, the significant adverse cost awards faced by would-be representative plaintiffs at the certification stage threaten to undermine the primary goal of class proceedings — access to justice. In response to a gap in the literature, and in light of the Law Commission of Ontario’s recent decision to undertake a review of class actions in Ontario, this paper provides a quantitative analysis of how cost decisions in the certification motion context have evolved throughout the life of the Class Proceedings Act, 1992 and focuses on whether the current costs regime is inhibiting access to justice. The data confirms that the quantum of cost awards has been increasing over time, especially during the last eight to ten years, and that the Class Proceedings Fund and section 31(1) of the Class Proceedings Act, 1992 have not consistently succeeded in protecting plaintiffs from the potentially negative impact of these rising costs. In light of these concerns, the paper concludes with recommendations that have the potential to help maintain the viability of the existing costs regime while preserving access to justice.

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 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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.643
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.197
GPT teacher head0.383
Teacher spread0.186 · 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 teacher head, not a consensus.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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