Quand l’informatique soulève des questions épistémologiques dans le domaine de la littérature de langue bretonne : l’exemple de la base de données PRELIB
Bibliographic record
Abstract
PRELIB est une base de données qui permet la consultation et le traitement de données en lien avec les acteurs du monde de la littérature de langue bretonne des origines à nos jours. Par une collaboration interdisciplinaire alliant littérature, sciences sociales et science informatique, il s’agit de mener l’étude des relations entre la littérature bretonne et d’autres littératures, en particulier la littérature de langue française, et des relations internes au champ littéraire breton. Il s’agit notamment d’identifier les lieux de sociabilité et les réseaux dans lesquels s’inscrivent les acteurs et producteurs du champ. L’article propose de retracer certains des questionnements que l’élaboration de cette base de données a fait apparaître, en l’occurrence : qu’est-ce qu’une œuvre, qu’est-ce qu’un auteur ou encore qu’est-ce qu’une relation entre individus ?
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 | 0.034 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.028 | 0.043 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".