Le plurilinguisme dans les productions audiovisuelles pour la diffusion et la vulgarisation de la science et de la technologie
Bibliographic record
Abstract
Dans ce travail nous nous focalisons sur le rôle du plurilinguisme dans la production d’une capsule vidéo pour la diffusion et la vulgarisation d’une recherche scientifique dans le cadre d’un événement promu par une université brésilienne. Devant l’usage privilégié du portugais (langue nationale) et de l’hégémonie de l’anglais dans la production scientifique, nous montrons l’importance de l’intégration du plurilinguisme pour une construction plurielle des savoirs. Le compte rendu de cette expérience a pour but de contribuer à l’amélioration des pratiques communicatives de l’enseignement supérieur qui visent à atteindre un public non spécialisé.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".