Étudier la transmission littéraire à l’ère du numérique : des grands écrivains à l’analyse des cocitations
Bibliographic record
Abstract
Du point de vue théorique, cette étude se trouve au croisement de la sociologie de la littérature et de la scientométrie. Grâce à l’intégration de méthodes quantitatives et en s’appuyant sur la loi des grands nombres, cette recherche a pour objet l’analyse des cocitations entre des écrivains appartenant à différentes époques et latitudes. À travers la compilation de plusieurs indicateurs, il est alors possible de visualiser le réseau de relations entre les prédécesseurs, les successeurs et les écrivains contemporains, permettant ainsi de mieux comprendre le phénomène de la transmission littéraire. De plus, cet article est une démonstration de comment l’analyse des métadonnées constitue une approche novatrice qui implique un changement épistémologique en études littéraires.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".