Bibliographic record
Abstract
L’ouvrage dirigé par Jean Mongrédien – Le Théâtre-Italien de Paris. 1801-1831 – et le site internet qui l’accompagne permettent de mener des recherches sur les mots utilisés dans les périodiques du début du XIXe siècle pour rendre compte de l’activité de cette institution lyrique parisienne. Une recherche « sédu* » donne pour résultats 424 occurrences de mots commençant par ces quatre lettres (séducteur, séductrice, séduire, séduite, séduisant…). On s’intéressera à l’usage global qui est fait de ces termes dans le corpus réuni par Jean Mongrédien (fréquence, définition des termes et des objets qu’ils qualifient) avant d’aborder trois thèmes majeurs qui apparaissent au cours de leur analyse : la place de la séduction dans les livrets des œuvres jouées au Théâtre-Italien ; la séduction des interprètes ; et celle opérée par la musique.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".