1. Evolving perspectives on learning French as a second language through immersion
Bibliographic record
Abstract
Ce chapitre passe en revue la recherche empirique ayant contribué à modeler les perspectives changeantes de l’enseignement immersif depuis la mise en place des programmes d’immersion française à Montréal il y a plus de 40 ans déjà. La recherche montre que les élèves en immersion française atteignent des niveaux élevés d’aptitudes pour la compréhension et des niveaux fonctionnels en production, avec des lacunes sur les plans de la précision de l’expression, de l’expression idiomatique, de la variété lexicale et de l’adéquation sociolinguistique. Ces lacunes seront expliquées en termes de contraintes résultant d’une interaction des propriétés structurales des traits problématiques de la langue seconde tels que les verbes, les pronoms et le genre; de leur prégnance dans le discours en classe, et d’une diversité de facteurs internes et reliés à l’apprenant. S’appuyant sur les thèses mises de l’avant par Lyster (2007) portant sur l’enseignement et l’apprentissage des langues axés sur le contenu, ce chapitre soutient le point de vue selon lequel plusieurs des lacunes dans la compétence en langue seconde chez les élèves en immersion française pourraient être surmontées par un enseignement qui saurait intégrer plus systématiquement langue et contenu. This chapter reviews empirical research that has helped to shape evolving perspectives of immersion education since the introduction of French immersion programs in Montreal more than 40 years ago. Research confirms that French immersion students attain high levels of comprehension abilities and functional levels of communicative ability in production, with shortcomings in accurate and idiomatic expression, lexical variety, and sociolinguistic appropriateness. Shortcomings will be explained in terms of processing constraints that result from an interaction among the structural properties of problematic target features such as verbs, pronouns, and gender; their degree of salience in classroom discourse; and a range of learner-internal factors. Following an argument developed by Lyster (2007) with respect to teaching and learning languages through content, the specific argument put forth in this chapter is that many shortcomings in the second language proficiency of French immersion students could be overcome through instruction that is counterbalanced in a way that more systematically integrates language and content.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".