Table générale des mélanges et hommages en droit francophone. Version 1.240 imprimable en 5 volumes.
Bibliographic record
Abstract
Les mélanges juridiques sont des témoignages d’amitiés et de respects offerts à de prestigieux juristes de tous horizons. Par leur nombre, leur unité profonde dans leur grande diversité, ils attestent pensons-nous de la transcendance de l’Institution universitaire. La présente table s’efforce de rassembler les sommaires du maximum d’entre eux. Elle indexe à ce jour le sommaire de 1240 mélanges juridiques en droit privé et sciences criminelles, droit public, histoire du droit et des institutions, sciences politiques, philosophie du droit et droit canonique. (France, Belgique, Suisse, Québec, Bénin, Liban, Algérie, Tunisie, Maroc, Côte d’Ivoire, Cameroun…). Son objectif est d’offrir un outil de recherche complet et libre d’accès. Et ses contributeurs osent appeler ça une mine… Une mine d’informations dans laquelle les chercheurs pourront descendre afin de trouver de précieux filons pour leurs recherches. Des mises à jour fréquentes sont réalisées et accessibles sur https://melanges.org ainsi que sur https://hal.science/hal-05034957 ISBN 979-10-983215-3-5 https://www.sudoc.fr/297675796 https://search.worldcat.org/fr/title/1598204572 contact@melanges.org
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.416 | 0.149 |
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".