Bibliographic record
Abstract
Cet article a pour but : 1 ) défaire le point sur l'état actuel des consensus en matière de définition des critères de qualité des dictionnaires selon les types de publics demandeurs ; 2) de dresser un tableau des paramètres significatifs en fonction des types de supports ; 3) d'analyser les effets des nouveaux outils technologiques sur les objectifs et sur ¡es pratiques dictionnairiques. La perspective adoptée découle de trois types d'expériences analysées sous l'angle de la pratique et de la formation : l'expérience du terminologue, du traducteur et du technicien. Elle conclut à une modification radicale, à très brève échéance, de la conception de tous ¡es dictionnaires, de la relation des utilisateurs à ce type d'outils et de leur place même dans la future chaîne des outils langagiers. Elle appelle ainsi à une urgente réévaluation des principes du consensus dictionnairique.
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 | 0.033 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".