Pourquoi une langue emprunte-t-elle des suffixes ? L’exemple du grec et du latin
Bibliographic record
Abstract
Afin de déterminer les raisons pour lesquelles le grec a emprunté des suffixes au latin, nous examinons, en suivant le cadre théorique de Danielle Corbin, le suffixe-(i)ár(is)< du latin ‑arius, par exemple dansvromiaris[‘malpropre’], qui construit des adjectifs dénominaux à caractère [-savant/-soutenu]. En particulier, les adjectifs en-(i)ár(is)attribuent d’une manière permanente une qualité péjorative qui, dans le cadre de l’activité humaine quotidienne, dévie de la norme sociale d’une manière perceptible directement par les sens. Ce trait, lié à leur registre, résulte du fait que le suffixe est emprunté au latin, une langue sans prestige aux yeux des Grecs. Cette représentation stéréotypique de la latinité permet au grec de marquer les différences entre, d’un côté, le [+soutenu], l’officiel, l’objectif et, de l’autre, le [-soutenu], le quotidien, le subjectif, en conservant, dans le premier cas, les éléments d’origine grecque, et en utilisant, dans le second, des éléments empruntés.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".