Bibliographic record
Abstract
Cette contribution analyse la nature fondamentalement complexe de la langue médicale. Les auteures montrent que cette complexité permet la densification du contenu informationnel et donc une transmission efficace des informations entre spécialistes. Toutefois, certains aspects de cette complexité peuvent constituer un obstacle entre les professionnels de la santé et les profanes, ainsi qu’entre les spécialistes eux-mêmes. Les auteures proposent alors la notion d’hypercomplexité, pour cerner les cas où la complexité perd sa fonctionnalité. La frontière entre complexité et hypercomplexité est une question de (dis)proportion entre le contenu notionnel d’une forme linguistique et l’effort requis pour la comprendre. Nous proposons alors (d’établir) une échelle de complexité, qui est liée, d’une part, à l’opacité imposée au destinataire du texte et, d’autre part, à la typologie à laquelle le texte appartient. La portée des choix rédactionnels a été mise en évidence à partir d’un corpus de textes de vulgarisation (formulaires de consentement éclairé et notices pharmaceutiques).
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".