Building Intercultural Capacity in School Teams to Support Refugee Students
Bibliographic record
Abstract
In a period of globalization and forced migration, refugee numbers are increasing exponentially, and unprepared school systems embrace students as families settle in unfamiliar territory. This Organizational Improvement Plan (OIP) explores the experiences of a school team at Calluna Elementary School (CES, a pseudonym) in Southern Ontario, where staff strive to build their collective intercultural capacity in order to best serve an influx of newcomers who have survived war and significant loss. The Problem of Practice (PoP) involves addressing staff struggles with trauma-informed pedagogy, early literacy instruction, and maintaining an asset-focused perspective, through a refugee critical race theory lens. To inspire radical change in the current organization, and to flex with the rapidly changing demographics of the school community, the principal adopts both a transformative and adaptive leadership approach. While the organization evolves and oppressive programs and practices are identified and addressed, a change plan and communication plan are applied. Implementing formal professional learning sessions for staff through a 4C framework will be instrumental in developing culturally sustaining practices which adequately provide essential supports for refugee students. Training for the school team which focuses on developing intercultural competence will improve the ability of the system to address the unique challenges encountered. This morally imperative work is applicable to school contexts around the world where refugees are accepted and barriers are faced when supporting effective settlement for newcomers.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.002 | 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".