Early French immersion in British Columbia: A Consideration of the 'Struggling Learner'
Bibliographic record
Abstract
Early French Immersion (EFI) is a popular optional education program offered inmost of the 60 school districts in British Columbia (BC), Canada. Though districtshave varying policies, the model generally involves entry into the program inKindergarten-Grade 1 where French is the language of instruction for all subjects.There are no prerequisites to enrolment other than the students age, available space,and parental choice. In the first years of EFI, French is one hundred percent thelanguage of instruction.The problem being investigated and reported upon is a result of a recurring andperplexing personal observation of a small cohort of learners in the researchersGrade 2 classroom. These learners presented a disparate profile that is irrespective ofability, but that shares one thing: the learners repeatedly struggle to meet expectationsdespite the teachers best efforts.The investigation aimed to determine if this is a phenomenon experienced byother teachers, and if it is, to initially open the discussion about these students, beginto understand the reasons behind the struggling, and identify and initiate ways tobuild the learners success and well-being. Specifically, this chapter will consider thequestion: Within the BC classroom context, to what extent are teachers identifyingstudents who struggle in Early French Immersion?
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".