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Record W4413243903 · doi:10.3138/ccar.v17i2.003

Fair Compensation or Unjustified Temptation to Compromise?: An Empirical Review of Requests for Honorarium Awards in Canadian Class Actions

2022· article· en· W4413243903 on OpenAlexaboutno aff

Bibliographic record

VenueCanadian Class Action Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsPlaintiffClass actionCompromiseCompensation (psychology)PaymentTemptationClass (philosophy)Economic JusticeLawAction (physics)Empirical researchConflict of interestPolitical sciencePublic relationsLaw and economicsBusinessSociologyPsychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Abstract: Every class action requires at least one representative plaintiff to bring the action, perform certain litigation duties, and represent the interests of the class members. If an action results in a successful outcome for the class, courts may award the representative plaintiff an honorarium as recognition for their contributions. The practice of approving honorariums raises significant policy issues, however. On one hand, judges have justified honorariums payments as appropriate compensation for a representative plaintiff’s service on behalf of others. On the other hand, judges are wary of encouraging the hiring of professional plaintiffs and cautious of creating a perceived or real conflict of interest between representative plaintiffs and the absent class members. In this paper, a comprehensive analysis of the role of representative plaintiffs is undertaken to explain the divergent and unpredictable approaches to honorarium requests taken by courts across Canada’s class actions landscape. Based on an empirical review of 116 published decisions from 2010 to March 2021, this paper argues that there should be a less restrictive and more uniform approach to awarding honorariums in accordance with principles of fairness and the critical purpose of enhancing 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)0.002
metaresearch head score (Gemma)0.000
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 score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.220
GPT teacher head0.468
Teacher spread0.248 · 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
Published2022
Admission routes1
Has abstractyes

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