L'informatique juridique : en progression vers un processus d'intelligence artificielle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it