Exploring Language Learning Experiences of Kurdish and Turkish Asylum Claimants in Canada Through Arts-Informed Research
Bibliographic record
Abstract
According to United Nations High Commissioner for Refugees, the highest level of displacement on record was reached in 2019 with 79.5 million people being forced to migrate (United Nations High Commissioner for Refugees, 2020). Although there is a significant influx of asylum seekers arriving in Canada from Turkey, there have been no studies completed in Canada that focus on the experiences of Kurdish and Turkish refugee claimants. Asylum seekers’ lived experiences need to be further investigated because their precarious legal status together with changing governmental policies may limit their access to language education and resettlement programs. Grounded in the arts, this paper analyzes the role of languages and language-learning experiences on Turkish and Kurdish asylum seekers and their integration into Canadian life. Participants were given the opportunity to document their challenges, needs, concerns and successes related to their integration and language-learning experiences by creating different art forms. Analysis of the data is made through the lens of Cooper’s (2011) Bridging Multiple Worlds Theory and Freire’s (1972) concept of critical consciousness. This research will be informative for educators and policy makers involved in the education of young adults from refugee backgrounds.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.033 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".