Bibliographic record
Abstract
Exposé de quelques problèmes qui se posent lors de l'indexation thématique d'un texte et de la recherche de contextes correspondant à un thème donné. Cet examen conduit à s'interroger sur la notion même de « thème » et à observer notamment le fonctionnement du mot thème aux XIX e et XX e siècles, dans le corpus de la base de données textuelles FRANTEXT : thèmes épisodiques, historiques, existentiels,... il s'agit toujours de pôles d'attraction plus ou moins spécifiques et susceptibles de déclencher une quelconque activité, souvent de réflexion ou de représentation, traduite fréquemment par un message oral ou écrit caractérisé lui-même par la récurrence des allusions aux dits thèmes. La compréhension du texte passe par le relevé des thèmes qui y sont traités, celle de chaque thème par l'étude des contextes qui l'illustrent.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".