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Record W4413722868 · doi:10.3138/ccar.v19i1.39

The Commonality Test in Class Action Certification: The Battleground of Screening Merits and Fair Process

2023· article· en· W4413722868 on OpenAlexaboutno aff
Mohsen Seddigh

Bibliographic record

VenueCanadian Class Action Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDispute Resolution and Class Actions
Canadian institutionsnot available
Fundersnot available
KeywordsCertificationClass actionTest (biology)Class (philosophy)Process (computing)Action (physics)Computer sciencePolitical scienceLawArtificial intelligenceProgramming languagePhysicsBiology

Abstract

fetched live from OpenAlex

The class action certification test requires “claims [that] … raise common issues.” The common law jurisdictions in Canada have become divided as to the evidentiary basis in fact required to establish that requirement. Some have recently held that the plaintiff must adduce some basis in fact that: (a) the proposed issues are common to the class; and (b) the proposed issues exist. Others have required evidence of commonality only, having identified incongruencies in principle in requiring evidence of the existence of issues proposed to be common to the class. This article takes a closer look at what it means to superimpose an evidentiary burden of existence on the commonality inquiry. The article reviews a sample of certified common issues, concluding that the existence inquiry is at its core a screening of the substance and merits of the common elements of the claim. In some cases, that merits screening is procedurally harmless. However, that screening often becomes incompatible with other certification caselaw that has adopted a rigid exclusionary approach to pre-certification discovery and an exacting test of evidence admissibility. The end result, when these lines of authority operate together, is a compromise in fair process: a process that demands evidence, and only evidence that would be admissible at trial, that it denies discovery of, even in cases where the evidence is solely in the possession and control of the defendant. This article calls for a holistic, rather than a piecemeal, approach to these elements of certification so that merits screenings — if they are desirable and permitted — are conducted openly and with fair and proportionate procedural safeguards.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.841
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.103
GPT teacher head0.324
Teacher spread0.221 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2023
Admission routes1
Has abstractyes

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