Giving Refugee Students a Strong Head Start: The LEAD Program
Bibliographic record
Abstract
As the complexity and cultural diversity in contemporary Canadian schools increases, educators are challenged to respond to the unique cultural, socioemotional, and learning needs of students whose families are fleeing hardship, global conflict, or persecution to seek safe haven in Canada. Like those in most major urban centres in Canada, schools in Calgary have received many newcomer students as families settle into their new lives. Teachers are tasked with providing high-quality education to learners from multiple language bases with limited formal education, little or no English, and histories of loss, grief, and/or trauma. The complexity of this task often requires a multidisciplinary team approach to support the teacher and address unique student needs. This article, written by an ESL teacher and a psychologist, provides an overview of one program that helps set the stage for success in Canadian classrooms. The LEAD program uses a three- pronged approach to foster successful inclusion, an approach comprising English language development, trauma-informed practice, and cultural responsiveness. L’augmentation de la complexité et de la diversité culturelle dans les écoles canadiennes met au dé les enseignants qui doivent réagir aux besoins uniques culturels, socio-émotionnels et pédagogiques de leurs élèves dont les familles fuient des difficultés, des con its mondiaux ou la persécution et cherchent refuge au Canada. Les écoles à Calgary, comme celles dans la plupart des grands centres urbains au Canada, ont accueilli plusieurs nouveaux élèves dont les familles s’installent dans leur nouvelle vie. Les enseignants sont chargés d’offrir une éducation de haute qualité aux apprenants ayant des bagages linguistiques divers et une scolarité formelle limitée, connaissant peu ou pas l’anglais et ayant subi des pertes, des chagrins et des traumatismes. La complexité de cette tâche exige souvent une approche collaborative et pluridisciplinaire pour appuyer l’enseignant et répondre aux besoins uniques des élèves. Cet article par une enseignante d’ALS et une psychologue présente un aperçu d’un programme qui prépare le terrain pour la réussite dans les salles de classe au Canada. Le programme LEAD repose sur une approche à trois volets qui favorise l’insertion en visant le développement de la langue anglaise et qui emploient des pratiques qui tiennent compte des traumatismes et qui sont adaptées à la culture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".