Bibliographic record
Abstract
L’analyse du discours intéresse des chercheurs d’horizons divers. Comme les nombreuses influences disciplinaires de l’analyse du discours s’entrelacent de plus en plus, les choix méthodologiques se multiplient. Comprendre les choix méthodologiques possibles, sélectionner le plus adapté et trouver des références devient ainsi parfois ardu. Cet article constitue une réflexion sur les choix méthodologiques qui s’offrent aux analystes du discours et propose une méthodologie qui pourrait convenir à ceux dont les travaux sont de nature inductive. Nous montrons ainsi que l’approche de la MTE constitue une avenue intéressante car elle s’enracine dans les données, ce qui permet au chercheur de mettre le discours au centre de sa démarche. Le retour réflexif que nous faisons sur un projet de recherche terminé permet également de réfléchir de manière plus concrète aux problèmes que peuvent rencontrer les chercheurs durant leurs travaux de recherche.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".