Migrant Adult Language Learning in a Transnational Context
Bibliographic record
Abstract
This presentation reports on the preliminary stages of a multi-year adult literacy project. The project is motivated by the intensity of contemporary global migration, where unprecedented numbers of adult migrants are forced into linguistically alien terrains and are often isolated and disadvantaged by language barriers preventing full participation in host societies. The project responds to this challenge with a comparative analysis of the formal and informal language learning experiences of adult migrants in three transit or destination countries characterized by an influx of newcomers: York Region, Ontario, Canada; Erie County, Pennsylvania, United States; and Agrigento, Sicily, Italy. Using a hybrid theoretical framework linking transnationalism (Glick Schiller, Basch & Szanton Blanc, 1995) and translanguaging (Otheguy, García & Reid, 2015), the project undertakes a fluid and multidirectional study of migration and conducts an analysis directed at the first-person experiences of language use in linguistically diverse contexts. Drawing on surveys and interviews, the project assesses migrants’ priorities for language learning, their agency in choosing language-learning opportunities and how language learning serves their needs. Supplementary perspectives from adult education providers and academics in migration-related fields inform evidence-based pedagogical and policy recommendations for how language-learning opportunities can support the social integration of adult migrants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| 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".