Omnia appetunt bonum: l'interprétation de la Lectvra cvm qvestionibvs in Ethicam Nouam et Veterem du Pseudo-Johannes Peckham
Bibliographic record
Abstract
Este artigo examina a primeira Lectio de um comentario da Etica Nicomaqueia escrito cerca de 1240-124 por um Mestre de Artes de Paris (Pseudo-Peckam). O adagio « appetunt » (todas as coisas desejam o bem), que se encontra nas primeiras linhas da Etica e ai analisado minuciosamente. Outras interpretacoes ao longo do seculo XVIII continuam ou transformam a elucidacao de Pseudo-Peckam. Seus argumentos sao reconsiderados frequentemente pelos comentadores posteriores. Essa Lectio e relacionada a outros textos da Faculdade de Artes atraves do tema do homem como um microcosmo. Abstract This paper examines the first Lectio of the 'Pseudo-Peckham's commentary on Nicomachean Ethics, written ca.1240-1244 by a Parisian Arts master. The adage omnia appetunt bonum (all things strive for the good) that lies in the first lines of the Ethics is there meticulously analysed. Other interpretations all along 13 th century continue or transform the Pseudo-Peckhams' elucidation and his arguments are often reconsidered by later commentators. This Lectio is related to other texts from the Arts Faculty through the subject of man as a microcosm. Resume Cet article examine la premiere Lectio d'un commentaire de l'Ethique a Nicomaque ecrit vers 1240-1244 par un maitre es arts de Paris (Pseudo-Peckham). L'adage « appetunt » (toutes choses desirent le bien) qui se trouve dans les premieres lignes de l' Ethique y est analyse minutieusement. D'autres interpretations au long du XIII e siecle continuent ou transforment l'elucidation du Pseudo-Peckham, ses arguments sont reconsideres souvent par les commentateurs posterieurs. Finalement, cette Lectio se relie a d'autres textes de la Faculte des arts a travers le theme de l'homme microcosme.
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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 | 0.002 | 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 | 0.007 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".