Supporting Adult Learners with Refugee Experiences through English Language Instruction
Bibliographic record
Abstract
Canada welcomes around 24,000 refugees annually (Citizenship and Immigration Canada 2015; 2016). Many adult learners with refugee experiences join English as an Additional Language (EAL) classes every year, whether these classes be federally or provincially funded. These adult learners with refugee experiences bring to EAL classes varied educational and life experiences. Some of these learners have little or interrupted schooling (Finn, 2010). Learners with this profile may have also encountered forced displacement, loss of identity, torture, and trauma. These experiences, along with post-traumatic stress disorder (PTSD), which some people with this background may suffer from, can lead to concentration difficulties and memory loss (Hauksson, 2003). This, in turn, can negatively impact additional language acquisition (Finn, 2010). When EAL instructors are unaware of refugee experiences, they may find it difficult to deal with these circumstances appropriately, which may create uncomfortable situations both for learners with refugee experiences and their instructors in class. To work with such learners, it is important that EAL instructors be very skilled, experienced, and patient. The present article provides readers with an opportunity to understand various refugee experiences, the acculturation process these learners may go through, and lesson planning strategies that can be incorporated when supporting adult learners with refugee experiences.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".