Entretien de Hela Zahar, rédactrice invitée du n° 13 (2025) de la Revue internationale des francophonies, professeure agrégée et responsable du Pôle d’études et de recherche en cultures numériques à l’université de l’Ontario français, avec Will Straw, professeur émérite au Département d’histoire de l’art et communications de l’université McGill à Montréal
Bibliographic record
Abstract
La Revue Internationale des Francophonies cherche à promouvoir le français comme langue-culture à l’attention (comme lecteurs et auteurs) de ces différentes couches de locuteurs « au nom de la diversité des langues et des cultures » (Hagège, 2006) avec les visions du monde et les ontologies que cette langue structure différemment de l’anglais notamment. Ainsi, la revue promeut l’idée que l’on se fait d’une langue (une culture, une histoire, une vision du monde…) plutôt qu’une langue arrachée de son substrat comme l’anglo-américain quand il devient langue de seule communication. En défendant le français comme culture, histoire… on défend toutes les langues... dont l’anglais.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".