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Record W4413243876 · doi:10.3138/ccar.v17i2.035

Two Steps Forward, One Step Back: “Some Basis in Fact” and the Certification of Common Issues

2022· article· en· W4413243876 on OpenAlexaboutno aff
Jacob Medvedev, R. Sutton, Linda Fuerst

Bibliographic record

VenueCanadian Class Action Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsPlaintiffCertificationLawPropositionBasis (linear algebra)JurisprudenceClass (philosophy)Supreme courtTest (biology)Common lawLegislationPolitical scienceLaw and economicsComputer scienceSociologyEpistemologyMathematicsPhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract: In this paper, the authors review the state of the law regarding plaintiffs’ evidentiary burden at certification. Specifically, the authors canvass the jurisprudence on the “some basis in fact” standard that plaintiffs are required to satisfy in respect of the common issue criterion under class proceedings legislation. Originally pronounced by the Supreme Court of Canada in Hollick v Toronto (City), the “some basis in fact” test has become the source of considerable debate. Two dominant perspectives have emerged over time. According to the first, the “some basis in fact” standard is a one-stage analysis that requires examination of only the class-wide nature of the proposed issues. According to the second, the “some basis in fact” inquiry involves a two-step analysis requiring plaintiffs to furnish evidence in support of the (1) existence of the alleged common issues and (2) the proposition that the issues can be resolved on a class-wide basis. By tracing the evolution of the “some basis in fact” test and analyzing comparative case law, it appears that the law is now settled: the two-step test governs the assessment of common issues at certification.

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 score0.983
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.083
GPT teacher head0.362
Teacher spread0.279 · 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
Published2022
Admission routes1
Has abstractyes

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