Portfolio langagier : Les finissants des programmes d’immersion se révèlent
Bibliographic record
Abstract
This article presents a research conducted with French Immersion graduates in the context of an introductory language methodology course in a francophone institution. The participants completed a language portfolio, created by Laplante & Christiansen (2001). The portfolio consisted of a two-page autobiographical narrative entitled 'My Life in French till now', an action plan which included an analysis of the errors they identified as targets for that semester and the means or the tools they chose to correct the errors. An analysis of the autobiographical narrative, a document written by the individual participants, stemming from their life experiences since they were exposed to the French language, is presented. The narrative is inspired by the values they have developed and the choices they have made concerning learning French (Baumeister, 1991; Kenyon, 1999). This study also presents a profile of significant experiences these French Immersion graduates identify as pivotal in their motivation to pursue their postsecondary studies in French. They also highlight teachers, friends and family who played an important role during 'their lives in French' till now. In their action plan, the students identified categories of linguistic challenges in areas such as semantics, syntax and phonology. These results shed light on questions pertaining to best practices in French Immersion programs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".