Trial Delay Caused by Discrete Systemwide Events: The Post-Jordan Era Meets the Age of COVID-19
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".