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Record W7128182976 · doi:10.3138/ccar.v10i1-2.81

Ontario’s Class Proceedings Fund: Separating Fact from Fiction

2015· article· en· W7128182976 on OpenAlexaboutno aff
Gina Papageorgiou

Bibliographic record

VenueCanadian Class Action Review · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDispute Resolution and Class Actions
Canadian institutionsnot available
Fundersnot available
KeywordsClass (philosophy)Class actionAction (physics)Third partyDisbursementSustainability

Abstract

fetched live from OpenAlex

Ontario’s Class Proceedings Fund (CPF) provides disbursement support for class actions initiated in Ontario. It was established in 1992 as part of Ontario’s class action regime with the goal of increasing access to justice. During the twenty years since it was established, there have been concerns expressed about whether or not it has fulfilled its intended function. Recently, third party funders have entered the marketplace, and some members of the bar have questioned the CPF’s continued relevance. Others have questioned the CPF’s sustainability given increasing costs awards. This article attempts to critically address these issues with reference to the historical data. It argues that the CPF has enhanced class members’ abilities to more fully pursue their lawsuits in Ontario compared to other forms of funding. To date, third party funding has not significantly affected the CPF as third party funders operate in a different market from the CPF; many of the types of cases that the CPF supports are unlikely to attract third party funding. Further, the CPF offers a method of funding that has the least potential for conflicts of interest — an issue that is currently of significant concern with respect to third party funding arrangements. This article concludes by arguing that for the foreseeable future, the CPF will remain sustainable and will continue to be an important part of the class action framework in Ontario.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.117
GPT teacher head0.293
Teacher spread0.177 · 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; both teacher heads agree on what is shown here.

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
Published2015
Admission routes1
Has abstractyes

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