Le temps recouvré : de la pertinence d’une constitution de corpus; méthodologie et usage
Bibliographic record
Abstract
Dans l’analyse, en syntaxe historique, d’un ancien état de langue, les attentes récentes de la théorie ne sont pas toujours comblées par les études et les grammaires traditionnelles de l’ancienne langue. Dans notre étude diachronique du français, nous devons donc mettre sur pied un corpus tiré de textes médiévaux afin d’accéder directement aux données qui répondent à nos questionnements théoriques. Le présent article explique les justifications pour la création de notre corpus du moyen français et pour le choix des textes qui le composent. On y présente d’abord les étapes qui permettent un traitement informatisé pratique et rapide du corpus, et ensuite les possibilités qu’offre ce traitement.
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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".