HOW CAN WE USE WEBCT TECHNOLOGY TO IMPROVE THE MINORITY FRANCOPHONE AND FRENCH IMMERSION EXPERIENCE IN WESTERN CANADA?
Bibliographic record
Abstract
Whether one is talking of early, late or partial French immersion or minority francophone programs in western Canada a fundamental problem that plagues the typical minority francophone or immersion class is the limited opportunitiy for the students to communicate on social and academic issues using French. What I am proposing is a model that allows unlimited use of French whenever they wish using technology. For many years in western Canada I taught mixed classes of francophone and anglophone students in which the language of instruction was either French or English. The difference in academic performance of both francophone and anglophone students, when the assigned readings were done in either their first or second language of French or English, was understandably remarkable. Whereas the Quebecois students were linguistically secure in the French medium classes, they were reduced to linguistically and socially insecure roles in the English medium classes. The converse was true of the Anglophone students who became subdued and less confident as they moved from classes in which their
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".