Plurilinguisme du roman chinois francophone et approche de la diversité et de la pluralité en FLE
Bibliographic record
Abstract
Actuellement, neuf auteurs chinois écrivent une œuvre romanesque en français. Ils font agir les langues de leur répertoire (au moins deux langues, en l’occurrence le chinois et le français) pour produire une écriture plurilingue, pluriculturelle. Nous observerons de quelle manière ces écrivains exercent leur compétence plurilingue dans un corpus composé d’extraits de romans. Les différentes formes de plurilinguisme textuel de ces textes sont envisagées comme des ressources au service d’une éducation à la diversité culturelle et à la pluralité linguistique. French-Chinese novel’s plurilingualism and diversity and plurality approach in FFL Nine Chinese authors are currently writing novels in French. They use their language resources (Chinese and French at least) to produce plurilingual and multicultural texts. We will study how these writers use their plurilingual competence through different novel excerpts. These various forms of plurilingualism are considered as educational resources to learn about cultural diversity and linguistic plurality.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".