Bibliographic record
Abstract
L’ensemble de mes recherches porte sur l’étude de corpus oraux dont l’observation ne se départit jamais des contextes d’élocution, qu’ils soient culturels, sociologiques, historiques ou linguistiques. Travaillant sur du vivant et sur des mises en représentation linguistique, mon terrain de recherches est plus spécialement centré sur la Gascogne, où s’entremêlent le français, l’occitan et l’euskara; dans les discours produits, la transcription devient lourde de conséquence. Comme tout chercheur travaillant sur de l’immatériel, j’essaie de rendre accessible à la communauté scientifique les extraits oraux de mes corpus par un travail de transcription au plus près de l’enregistrement. Mais quand l’édition de témoignages sonores s’adresse à un large public, le chercheur est face à certains dilemmes, qui m’ont amené à conforter ou repenser certains choix dans la façon dont un « texte oral » devient un « texte écrit ».
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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.104 | 0.091 |
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".