Groundwork for Assessing the Legal Risks of Cyberjustice
Bibliographic record
Abstract
It is clear that the use of information technology is quickly becoming a necessity for the justice system. In civil cases, delays and costs are causing individuals to abandon the courts, and cases that make it to trial are of ever-increasing complexity. Moreover, public security is weakened by the inefficient and cumbersome conditions by which criminal justice information circulates among the various stakeholders, such as the police, prosecutors, the courts, penitentiaries and parole boards, to name only a few. It becomes apparent that information technology has much to offer individuals involved in court cases and the justice system as a whole.\nWe have every reason to think that this change in medium will affect law, in general, and rights, in particular. At the same time, legal systems can clearly be improved by the use of new information and communication technology. These two observations call for reflection on the implications of computerizing and networking the justice system, where the requirements of legal certainty — demanding a prospective study of the risks entailed by the change — will be met. It will also make it possible to guide the way in which computer potential is used in legal proceedings and justice information. To that end, we will sketch out the broad lines of a method for assessing legal and judicial risks flowing from the implementation of cyberjustice systems.
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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 | 0.024 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.004 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".