Migrants and Mobile Technology Use: Gaps in the Support Provided by Current Tools
Bibliographic record
Abstract
Our current understanding of how migrants use mobile tools to support their communication and language learning is inadequate. This study, therefore, explores the learner-initiated use of technologies to support their comprehension, production, and acquisition of English following migration to Canada. Information about migrant use of technologies and experiences was collected by interviews. The interview data was analysed through the complementary lenses of noticing, from language learning, and appropriation, from human-computer interaction. Combining these lenses enabled the identification of unmet migrant communication, support, and learning needs. The manner in which migrants employed mobile and other tools to facilitate their learning and communication were identified through the application of these theories. This analysis indicates that migrants can use existing tools to access information. However, they need additional support if they are to take full advantage of existing mobile tools. Moreover, there is a need for tools that support larger gaps in their knowledge and skills. Migrant experiences indicate that they need additional social, meta-cognitive, and emotional support. These needs suggest opportunities for creating mobile tools that scaffold the development of new skills that include the learner’s ability to monitor and plan his or her learning and understand language produced by those who speak different varieties of English or who have non-majority accents.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".