Bibliographic record
Abstract
Through technological developments, legal systems increasingly apply videoconference in their criminal proceedings. Videoconference enables a direct video and audio connection between those involved in criminal proceedings. With the improvement of videoconference technology, it is more and more considered as a reasonable alternative to physical appearance. The application of videoconference had in recent years already increased. The global COVID-19 pandemic prompted a heightened focus in the Netherlands as well as everywhere else in the world on the potential to hold criminal proceedings despite having to limit physical contact through the use of videoconference. This generated a need for further research for particularly its application for the accused, considering the implications for guaranteeing the rights of the accused and fundamental principles like immediacy and the public nature of a trial. The main question of this research is: “What can the Netherlands learn from national and international standards and practice of the use of videoconference for the accused with a view to its standardization and policy development in Dutch criminal legal practice?”National and international standards and practice have been mapped out by researching the regulations and practices of the Netherlands, Italy, France, Canada, Switzerland, Germany and international and transnational criminal law.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.137 | 0.052 |
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".