Bibliographic record
Abstract
Dans tous les traveaux sociolinguistiques, on peut observer deux versants, la societe et le language. L'approche ethnomethodologique a structure les relations sociales et les etudes du langage en situation. Les didacticiens de language ont introduit cette problematique sociologique pour produire la notion de competence communicative en situation dans le domaine des langues etrangeres, on a mis l'accent sur la notion de situation et celle d'authenticite. On conseille souvent de servir les documents authentiques pour completer l'absence de l'authenticite reelle. Nous avons choisi les meme documents authentiques qui sont utilises dans trois methodes differentes pour mettre en lumiere la relation entre la situation et l'authenticite, a savoir Bienvenue en France, Tempo et Cadences. Nous avons fait l'attention sur le fait qu'elles sont employees actuellement a l'Alliance Francaise Seoul ou on est en situation exolingue, Vancouver au Canada en situation bilingue et Paris en situation endolingue. Par l'analyse des documents authentiques, nous avons su que l'absence des activites authentiques ne peut pas accomplir la fonction de communication. Pour choisir une telle ou telle methode, nous nous permettons ici d'insister sur la notion du document authentique mais aussi sur celle d'activite authentique.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".