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Record W3127062795 · doi:10.60082/2817-5069.3607

Trial Delay Caused by Discrete Systemwide Events: The Post-Jordan Era Meets the Age of COVID-19

2021· article· en· W3127062795 on OpenAlexaffvenue
Palma Paciocco

Bibliographic record

VenueOsgoode Hall law journal · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsYork University
Fundersnot available
KeywordsAdjudicationCharterCoronavirus disease 2019 (COVID-19)LawLaw and economicsPolitical scienceSociologyMedicine

Abstract

fetched live from OpenAlex

Court closures necessitated by COVID-19 have resulted in extensive trial delay, with implications for the section 11(b) Charter right to be tried within a reasonable time. Although COVID-19 appears to be a straightforward example of an “exceptional circumstance” under the Jordan framework that governs section 11(b), careful analysis reveals that it falls within a category not contemplated by that framework—what this article calls “discrete systemwide events.” Because COVID delay impacts cases across the system, the reasonable steps that can be taken to reduce it are themselves largely systemic in nature. Crucially, the exceptional circumstances analysis stipulated by Jordan focuses exclusively on the steps available in an individual case, while systemic delay is addressed indirectly through presumptive ceilings. Because the presumptive ceilings were not calibrated with COVID-19 in mind, they cannot account for COVID delay. Nor can systemic responses to COVID delay be assessed as part of the general exceptional circumstances analysis: Such an approach would require judges to adjudicate the reasonableness of myriad institutional policies, giving rise to problems ranging from a lack of data to separation of powers issues. This conundrum points towards one of two extremes: discount COVID delay without a full Jordan analysis, thereby partially relieving the Crown of its burden to justify presumptively unreasonable delay and leaving accused persons to bear the cost; or effectively prevent Crowns from justifying COVID delay as an exceptional circumstance, thereby risking thousands of stayed criminal charges flowing from the pandemic. This article suggests an alternative approach that navigates between these extremes: In some instances, delay caused by a discrete systemwide event like COVID-19 should be remedied by a sentencing reduction, authorized either by the Charter or by the sentencing process set out in the Criminal Code. This solution, while imperfect, achieves a more palatable result while adding minimal complexity to the section 11(b) analysis. If adopted, it could save accused persons from disproportionately bearing the costs of COVID delay, which would be the likely outcome were the Jordan framework applied straightforwardly.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.078
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0110.008
Open science0.0020.008
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0070.001

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 head0.060
GPT teacher head0.417
Teacher spread0.357 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2021
Admission routes2
Has abstractyes

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