Digital Equity in Canada: A Mixed Methodological Study of Digital Access, Digital Use and Digital Empowerment for Immigrants, Youth and Young Newcomer English Language Learners
Bibliographic record
Abstract
This sequential mixed-methodological study explores the complexities of Internet use in Canada for immigrants and young people, with a particular focus on the digital lives of English Language Learner (ELL) newcomer youth. Incorporating data from the 2020 Canadian Internet Use Survey (CIUS), online surveys, and interviews with five ELL newcomer participants, the research challenges the existing literature by revealing that immigrant status may no longer be a predictor of digital inequity. Analysis of variance (ANOVA) with CIUS data comparing immigrants and non-immigrants indicates that immigrants report more digital access, skills, and engagement in capital-enhancing activities online compared to their Canadian-born counterparts. A second ANOVA comparing younger Canadians with all other age groups showed significant age-related differences in mean index scores with statistically significant differences in terms of digital access, skills and engagement in capital-enhancing activities. In all cases, youth means were higher than means for all other age groups. The second phase of the research delves into the nuanced experiences of five ELL newcomer youth, highlighting their high levels of digital access and skills while underscoring their limited participation in advanced digital activities due to self-reported language proficiency and safety concerns online. This study emphasizes the necessity of probing beyond surface-level digital activities to comprehend underlying factors influencing digital experiences. This research study offers critical insights at both macro and micro levels that contribute to advancing knowledge, informing policy, and improving practices related to digital equity and the needs of ELL newcomers in Canada.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".