Supporting Refugee Students in Canadian K – 12 Classrooms: A Seminar and Resource Toolkit for Pre-Service Teacher Candidates
Bibliographic record
Abstract
For well over a decade, the United Nations High Commissioner for Refugees (UNHCR) has sounded the alarm about the increasing rate at which conflicts, violence, fear of persecution and human rights violations are forcing people to flee their homes. Canada has been the top resettlement country for refugees for the past four years and in 2022 – 2023 welcomed over 175,000 Ukrainians displaced by war. Studies show that Canadian teachers report feeling underprepared to understand and serve the needs of refugee students because of limited access to targeted professional development opportunities and a lack of related teaching resources. This research aimed to address this gap by developing a resource that will help pre-service teacher candidates understand the unique needs of refugee students, and to raise awareness about critical factors regarding the educational integration of refugee and displaced students. The resource consists of a seminar presentation and a toolkit of supporting resources which may be presented within Canadian Faculties of Education, or it can be adapted for use as professional development session for practicing teachers or deployed online as a free additional qualification mini-course
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".