L’analyse documentaire et les langages de spécialité : un filon à exploiter ?
Bibliographic record
Abstract
Depuis plus de vingt ans, les recherches sur les langages dits de spécialité ont ouvert de nouvelles pistes dans l’exploration du langage naturel. De contenu délibérément homogène pour mettre en valeur le message qu’ils véhiculent, ils sont identifiables tant par leur structure que par leur syntaxe et leur lexique. À partir d’une expérience portant sur des rapports d’analyse environnementale et réalisée avec le logiciel SATO, différentes classes de termes tirées de ces textes sont récupérées. En sondant ainsi les mécanismes de composition des documents grâce au principe des langages de spécialité, il est possible d’entrevoir des améliorations aux techniques d’analyse documentaire.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".