The performance of the courts in the digital era: The case of insolvency and restructuring proceedings
Bibliographic record
Abstract
Abstract The performance of the courts has been at the center of both political and public debates around the world and is one of the success indicators in the development of public policies on access to law and justice, particularly as regards the right to obtain a decision in reasonable time. The introduction of new information and communication technologies has been essential in extending this access to law and justice in two ways: as a way of making courts function more efficiently; and as an instrument for measuring and assessing the level of compliance with established policy objectives. Anchored in this “computerization of justice” movement, we intend to analyze its role in the overall performance of the courts in an important and central area for the economy and the functioning of the markets: corporate insolvency and restructuring. In order to achieve this purpose, a series of interviews was carried out with several key judicial players and stakeholders. What has been found in this research shows that day‐to‐day reality is not yet fully in line with policy purposes and legal provisions. Technologies still play an ambivalent role: despite streamlining processes, they raise new difficulties and challenges that require constant improvements.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.025 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".