Élaboration du corpus DEMOCRAT : procédures d’annotation et d’évaluation
Bibliographic record
Abstract
S’il existe déjà plusieurs corpus annotés manuellement en expressions référentielles et en chaînes de référence, il n’en existe aucun pour la langue française, ou alors pour des annotations qui relèvent plus de l’anaphore que de la coréférence. Le projet DEMOCRAT a produit un tel corpus, avec qui plus est une dimension diachronique. Sa conception a posé un ensemble de difficultés non seulement linguistiques mais aussi au niveau de l’homogénéité des annotations, de leur vérification et de l’évaluation de leur qualité. C’est cette dimension que nous proposons ici d’explorer et de discuter, en nous focalisant sur les conventions d’annotation et l’évaluation des annotations obtenues, procédure impliquant un calcul de l’accord inter-annotateurs. Cet article met ainsi en perspective le contenu du corpus democrat , pour légitimer les exploitations qui en seront faites.
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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 | 0.032 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.016 |
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".