Discovering aspects of the multicultural character of Québec and Canada through language : designing task-based learning situations for the adult education course, Discovery and Challenges-ENG P 104-4
Bibliographic record
Abstract
This essay follows Paillé’s (2007) methodology to design two learning situations for the course “Discovery and Challenges- ENG P104-4” that I often teach to adult language learners at the EMSB. This is a course in the area of citizenship in the adult education sector where “Using language to discover Québec and Canada and their multicultural character” (BIM, 2013, p. 2) is the purpose of the course. The learning situations follow Douglas Brown's (2015) task-based teaching and learning approach in order to cover the four language competencies where the student: “Reads simple, everyday texts adequately; Understands simple, everyday oral texts adequately; Writes simple, everyday texts adequately; and Interacts adequately in everyday situations using simple oral texts” (BIM, 2013, p. 2). The task-based activities for each competency match with the compulsory elements; I follow the Ministry's conceptual framework to design material for the course. The first learning situation is entitled “Applying for Canadian Citizen” where students accomplish tasks that inform them about the requirements and steps to apply. The second learning situation is entitled “Discrimination against the LGBT community” and introduces topics such as discrimination in the work place and the rights and responsibilities of Canadian citizens. After creating the learning situations and trying them out with my students, I gather and analyze feedback from two colleagues and 12 students. The essay ends with a reflection on the process, provides recommendation for teachers, indicates limitations as well as possible future directions; it presents the concept of senti-pensar by Galeano (2003) and explains how it can be integrated with Difrasismo, a dialectic from the Aztec and Mayan civilizations as a way of framing the emotional-language experience of immigrants who are learning to navigate language structures and cultural concepts.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.005 |
| 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".