Bibliographic record
Abstract
Le corps est la voie primordiale dans le processus d’énonciation, mais reste encore (trop) souvent corps étranger dans le processus d’enseignement/apprentissage des langues étrangères. Or entrer dans une langue étrangère, se l’approprier physiquement, sonner juste dans une autre langue, précisément pour qu’elle ne soit plus étrangère, implique un travail sur le corps, peut impliquer un transfert de corporéité. Oser les pratiques de corps dans la classe de langue : voie incontournable pour éviter les voix dissonantes. Dare the body voice Abstract: Focusing and working on the body is inescapable in the process of enunciation, but unfortunately not sufficiently put into practice in language learning. Speaking a foreign language, being attuned in that target language, i.e. to sound as close as possible to a native speaker, implies to work on the body physically as well as mentally, which means dealing with representation because of the transfer of identity the user may experience. Dare the body voice for a body change when switching from a language to another is a necessary direction the teacher ought to lead the learners to work on.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.015 |
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".