Individuation : repenser la biographie langagière pour accompagner l'articulation d'un soi francophone en contexte canadien de langue minoritaire
Bibliographic record
Abstract
Resume Tandis que de recentes recommandations officielles encouragent l’application du Cadre europeen commun de reference pour les langues (CECR) au contexte educatif canadien, il apparait necessaire de s’interroger sur les mises a jour a accomplir sur les outils concus par le Conseil de l’Europe—outils qui ont ete elabores sur une base theorique datant de plus d’une dizaine d’annees et dans un environnement politique et linguistique precis. C’est dans cette optique que cet article se concentrera sur la biographie langagiere du Portfolio des langues. Il tentera de demontrer qu’un support theorique adapte permettrait de reenvisager l’exercice pour maximiser ses avantages. En effet, depuis les etudes sur la narration en analyse de discours et en sociolinguistique, il est devenu evident que l’acte de se raconter ne peut pas etre apprehende comme un exercice factuel ou comme allant de soi. Il s’agit plutot d’un exercice socioculturellement marque qui se situe dans un contexte interactionnel et pedagogique precis ou l’apprenant de francais construit ce qu’il/elle definit comme etant son « histoire » et donc son identite francophone. Abstract Recent official recommendations to adopt the Common European Framework of Reference (CEFR) in a Canadian context have brought forward the necessity to reflect on the updates and adaptations to be made to the documents produced by the Council of Europe—documents that are based on a theoretical framework dating back to the 1990s and conceived for a different political and linguistic setting. In this paper, I will examine the language biography, part of the Language Portfolio, in order to show that an appropriate theoretical framework would permit us to rethink the exercise and to maximize its benefits. In light of recent developments in the field of discourse analysis and narration studies, it has become evident that the act of narrating one’s story should not be thought of as a straightforward exercise. It is a culturally marked behaviour that always takes place in a defined pedagogical context where the learner of French as a second language constructs what s/he sees as his/her story, therefore his/her francophone identity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".