Creating Inclusive EAL Classrooms: How Language Instruction for Newcomers to Canada (LINC) Instructors Understand and Mitigate Barriers for Students Who Have Experienced Trauma
Bibliographic record
Abstract
This article draws on my dissertation, “Creating Inclusive EAL Classrooms: How LINC Instructors Understand and Mitigate Barriers for Students Who Have Experienced Trauma.” The article explores some assumptions and understandings that English as an Additional Language (EAL) teachers bring to teaching students believed to have experienced trauma, and illustrates the dilemmas they face in supporting such students in a government-funded and designed EAL program for newcomers. Using the concept of Iris Marion Young’s “Five Faces of Oppression” (1990), the data and ndings of the research for my dissertation are explored, contributing to the discussion on trauma and learning in EAL programs and specifically in relation to adult immigrants and refugees. Cet article puise dans ma thèse « Creating Inclusive EAL Classrooms: How LINC Instructors Understand and Mitigate Barriers for Students Who Have Experienced Trauma ». L’article explore quelques hypothèses et interprétations que véhiculent les enseignants d’anglais comme langue additionnelle (ALA) à l’égard d’élèves qui ont subi des traumatismes d’une part, et il illustre les dilemmes auxquels font face les enseignants en appuyant ces élèves dans le cadre d’un programme d’ALA pour nouveaux arrivants et qui est nancé et conçu par le gouvernement d’autre part. M’appuyant sur le concept des cinq visages de l’oppression de Iris Marion Young (« Five Faces of Oppression », 1990), je me penche sur les données et les résultats de ma thèse, contribuant ainsi à la discussion sur le traumatisme et l’apprentissage dans les programmes d’ALA, notamment en ce qui concerne les immigrants et les réfugiés adultes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".