A female refugee’s investment in multiple literacies post-migration
Bibliographic record
Abstract
As the immigration and refugee intake rates continue to rise in Canada, English Language Learning (ELL) schools, centres and programs strive to keep pace with the demand. ELL educators are being propelled to think and teach in new ways that meet the needs of learners living in a digital age. Some learners arrive with competency in English language literacies and/or digital literacies, while others do not. For learners who possess minimal traditional print based and/or digital literacies, integrating into modern Canadian society can prove extremely challenging. This case study explores one such learner’s engagement with ELL and other literacies in a multicultural, modern urban centre on the West Coast of Canada. Semi-structured interviews, informal observations and conversations were the methods used to provide a holistic overview of the participant’s language learning process. The findings of this research demonstrate how identity is linked to investment in ELL as a means to increase economic, cultural and/or social capital. When the dominant ideology positioned the participant as an outsider because of her low level of proficiency in spoken English, she was prevented access to meaningful employment and denied a sense of independence, leading her to be creative in constructing an “imagined identity” that would better her life chances. Similarly, she was silenced and excluded from online spaces and membership in a discourse community because of a lack of digital literacy. The participant also struggled to “read” the sociocultural literacy of her new environment and felt positioned as an outsider, unable to judge situations and people accurately. While her English language literacy development was limited, relative to her classmates, over the course of her two-year study, she did eventually develop the sociocultural literacy necessary to evaluate her life prospects and construct a new identity, which led to an increase in her symbolic capital and overall well-being.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".