Bibliographic record
Abstract
Toute science connait de grands maitres, des erudits devoues ayant contribue d'une facon ou d'une autre de maniere extraordinaire a son developpement et qui ont transmis leur autorite aux generations suivantes. Le droit canonique ne fait pas exception. L'A. rappelle ici le souvenir de quelques-uns de ces grands. A l'aide d'anecdotes quelquefois humoristiques relatees par d'anciens eleves, par des membres de leur famille ou par des confreres et de notes biographiques interessantes, leur histoire est ici racontee. Ces canonistes du passe nous partagent leur esprit et entrouvrent ainsi une fenetre sur les seminaires et les universites a l'epoque de la promulgation du Code de 1917. Nous apprenons a les connaitre dans leur vie personnelle, spirituelle et professionnelle. L'article brosse le tableau biographique des geants suivants: Adam C. Ellis, S.J., T. Lincoln Bouscaren, S.J., Matteo Conte of Coronata, O.F.M. Cap., Gommarus Michiels, O.F.M. Cap., Siervo Goyeneche, C.M.F., Giacomo Violardo, Ludoviek Bender, O.P., William J. Doheny, C.S.C., Felice M. Cappello, S.J.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".