Une approche textométrique pour étudier la transmission des savoirs biologiques au XIXe siècle
Bibliographic record
Abstract
Cet article propose d’aborder la question du positionnement entre qualitatif et quantitatif (que suppose l’analyse informatisée de données textuelles) au travers d’exemples concrets tirés d’un projet de recherche se situant dans le domaine de la création littéraire et son rapport aux savoirs biologiques (Biolographes : http://biolog.hypotheses.org ). Une première partie expose les aspects pratiques des corpus numériques, de l’accès aux sources à leurs métadonnées, en passant par les questions d’océrisation et de stockage (base de données). Les deuxième et troisième parties illustrent la façon dont des outils textométriques et de visualisation (TXM, Treecloud) servent de point d’appui, dans le cas de grands corpus, à de nombreuses hypothèses de travail. En conclusion, il souligne le pont opéré par le TAL entre les outils informatisés et l’analyse littéraire.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".