Bibliographic record
Abstract
Faced with the COVID-19 pandemic, the judicial system has tried to minimise delays and lessen the impact of the crisis on litigants by showing openness by allowing, among other things, remote interrogations and virtual trials. The pandemic, as a potential catalyst for transformation, has justified and continues to justify a rapid shift in litigation towards the technological world. The changes experienced are a priori carriers of efficiency in the administration of justice, an efficiency necessary in times of pandemic austerity. Candid in the face of the changes experienced, the courts have shown themselves to be flexible and innovative, embracing new technologies in a judicious manner. So even though the pandemic has shown major cracks in the infrastructure of the justice system, it has also prompted a change in society’s relationship to justice. Faced with this state of the “new normal”, it is obvious that society can no longer retreat. The present article assesses the nature and extent of the transformation of civil justice and the changes in the law of evidence experienced in the times of the pandemic, in order to structure the normative framework applicable to evidence once the pandemic has ended. We discuss transformations of justice and inherent mutations in evidence, theory of change and evolution of rituals and fundamental principles of judicial law according to the pandemic. One conclusion suggests three broad guidelines for future evidence law, consistent with pandemic values.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.074 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".