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Record W4413244052 · doi:10.3138/ccar.v14i2.279

Assessing Fees when Class Actions Follow Government Action

2019· article· en· W4413244052 on OpenAlexaboutno aff
Misha Boutilier

Bibliographic record

VenueCanadian Class Action Review · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsClass actionPlaintiffGovernment (linguistics)EnforcementSupreme courtClass (philosophy)Economic JusticeAction (physics)LawLaw and economicsPolitical scienceBusinessEconomicsComputer science

Abstract

fetched live from OpenAlex

Abstract: The Supreme Court of Canada’s Sun-Rype and Fischer decisions have recently focused attention on the relationship between public enforcement and private class actions. This essay considers an under-studied aspect of that relationship; namely, how courts should calculate fees in a class action that follows and benefits from a prior government enforcement action. The author contends that class actions that follow government actions are less risky for class counsel and are inferior to independently initiated class actions at ensuring behaviour modification and access to justice. Accordingly, the author argues that Canadian courts should follow the US Courts of Appeals for the Second and Third Circuits and adopt a specific rule governing fees when class actions follow government action. This rule would both reward plaintiffs’ attorneys who independently initiate class actions with increased fees, and would reduce fees for class actions that follow government action in proportion to the benefit that class counsel receive from the prior government action. This rule will allow courts to appropriately assess risk in light of the reduced-risk profile that class actions that follow government action generally present. It will also encourage independently initiated class actions and thus help ensure behaviour modification and access to justice for wrongs in cases of government inaction.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.005

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 head0.278
Teacher spread0.170 · 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
Published2019
Admission routes1
Has abstractyes

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