Exploring the intersections of immigrant seniors’ digital literacies and social connectedness: a Canadian study
Bibliographic record
Abstract
Seniors’ adoption of emerging technologies is crucial to their social connectedness, well-being, and digital participation in society. This article presents a Canadian study on how immigrant seniors established and sustained social connections through their engagement with digital technologies during the COVID-19 pandemic. Specifically, we aim to (1) deepen understandings of how immigrant seniors’ learning through and about technologies can shed light on our conceptualization of seniors’ digital literacies and (2) suggest programs and pedagogies that could foster lifelong learning for seniors. Data were collected through interviews, observations, and digital artifacts from a sample of immigrant seniors (N:16). Through narrative, we stitch together the personal and sociocultural perspectives from four seniors’ stories for holistic insights into their learning and engagement with technologies. Their stories also emphasize possibilities for dynamic and interconnected digital engagement and the inseparable link between community support and developing seniors’ digital literacy. Social interaction plays a pivotal role in facilitating, fostering, promoting, and enhancing seniors’ digital literacies. Our findings challenge preconceived notions about how seniors navigate digital technologies and offer strategies for supporting community service agencies in designing and implementing senior-friendly digital literacy programs.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".