Bibliographic record
Abstract
Êtes-vous francophone ? Cette question qui parait à première vue extrêmement simple, est beaucoup plus complexe qu’elle n’en a l’air. Dans cet article, j’introduis la notion de francophonie(s), au pluriel et au singulier, que je revisite à partir du concept sociolinguistique d’espaces sur la base de quatre entretiens que j’ai menés avec des universitaires qui ont accepté de me donner leurs perspectives sur la question. L’analyse montre que la manière dont les individus se situent vis-à-vis de la francophonie dépend moins de la notion de compétence en français que de l’interprétation qu’ils ont du concept en interaction avec leur propre univers de référence. J’invite à entrer dans des pratiques pédagogiques de questionnement des idéologies, des univers de références, non pas pour les juger, mais pour permettre aux acteurs de l’éducation (élèves, enseignants, équipes scolaires) de s’approprier la francophonie en toute légitimité.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".