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Record W4413733799 · doi:10.3138/ccar.v19i1.79

Bridging Borders: Reforming Canada’s Approach to Resolving Carriage Challenges in Light of Global Practices

2023· article· en· W4413733799 on OpenAlexaboutno aff
Oscar Read

Bibliographic record

VenueCanadian Class Action Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCarriageBridging (networking)Political scienceRegional scienceEconomic geographyGeographyComputer scienceArchaeologyComputer security

Abstract

fetched live from OpenAlex

Canada faces a carriage crisis. Mass breaches frequently affect persons across Canada in each province. Each mass breach results in duplicative class actions filed in multiple provinces. The result is a provincial fight for carriage and a national fight for membership of potential class plaintiffs. Carriage within provinces is decided at a carriage motion; carriage on a national scale is decided on an ad hoc basis in the isolation of the superior court of whichever province the national class action(s) is/ are filed. In this essay, I take a comparative approach to proposing reform to provide a procedure for the efficient resolution of intra- and inter-provincial carriage disputes. I take stock of Canada’s current approach to resolving carriage disputes and then draw from the experience of other common law jurisdictions: Aotearoa New Zealand, Australia, the United Kingdom, and the United States. Drawing on their experience, I propose three changes: two legislative and one judicial. I propose first that each province enact legislation restricting provincial jurisdiction over class members; second, that each province enact legislation enabling the transferring of jurisdiction over cases between the provinces; and third, a judicial change from seeing comity as a principle of deference to seeing it as a principle of leadership and cooperation. The overall goal of the reform will be to centralise decisions within one province for the efficient resolution of carriage issues, both within and between provinces.

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)0.002
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.771
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.124
GPT teacher head0.382
Teacher spread0.258 · 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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