L'informatique juridique : en progression vers un processus d'intelligence artificielle
Bibliographic record
Abstract
This paper deals primarily with computer-assisted legal research. It attempts to sketch the current state of the art, mainly in the United States and Canada, with special reference to systems oriented towards the processing of legislative data. The author suggests a checklist of the main requirements the systems of the 80's will have to answer to, in order to fulfill the growing needs of the new computer-minded generations of law graduates. Along these lines, this paper deals also with the second generation systems dedicated to automated legal research ; these could be expected to show some form, albeit elementary, of humanlike intelligence. Four prototypes of such systems are considered; they are the American Bar Foundation's and Jeffrey Meldman's systems, as well as the well-known JUDITH and TAXMAN systems. The paper concludes on a glimpse of the Third Wave of computerized legal research, in the belief that the legal profession will meet the challenge of the computer age, will learn to live and work with this new technology, and will master the artificial but sometimes acute intelligence of our new friend, the Robot.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".