Hacia una digitalización de los sistemas judiciales centrada en las personas que fomente el empoderamiento, los nuevos derechos y el trato justo en medio de la brecha digital.
Bibliographic record
Abstract
Advanced digital tools used by justice systems, such as electronic communications, remote interactions, videoconferencing, remote access to judicial files, and interactions with online dispute resolution platforms, heighten users’ exposure to the digital world. This can be particularly challenging for self-represented litigants. As digitalization intensifies, addressing the digital divide and protecting rights in human–machine interactions are becoming increasingly relevant. In response to this digital shift, the idea that people should be at the centre of justice digitalization projects is gaining ground, prioritizing more than just the efficiency of the public justice system. The challenge is to substantiate this objective and translate it into concrete measures and actions that go beyond maintaining the protection of fundamental rights. This chapter aims to identify good practices, regulatory alternatives, and complementary measures to achieve a truly people-centred digitalization of justice. For this purpose, regulatory measures introduced in judicial systems in Europe (such as the European judicial cooperation and national developments in Estonia, the Netherlands, Spain, and the United Kingdom), Asia (China and Singapore), and the Americas (the United States and Canada) are reviewed. Models for electronic communications digitalization, the use of videoconferencing, the creation of online courts, and other actions to achieve a people-centred digitalization of justice systems are examined.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".