Who uses technology to socialize? Evidence from older Canadian adults
Bibliographic record
Abstract
Abstract Socializing is understood to be important for mental and physical health, especially in later life. Technology-mediated socializing may be just as beneficial, but older adults are less likely to adopt social technologies than younger cohorts. Using time use data from the Canadian General Social Survey collected in 2015–2016, the older adult population (65 +) is clustered into ‘tech socializers,’ ‘common socializers,’ and ‘in-person socializers’ using a k-means algorithm. We employ multinomial logistic regression to assess explanatory relationships for the assigned mode of socializing. Model results demonstrate that older adults with disabilities have lower odds of being in-person socializers and higher odds of being tech socializers. Older adults are also more likely to be in-person socializers in the summer and autumn months, but we observe no seasonal relationships for tech socializers. More research with longitudinal time-use data and more discrete conceptualizations of disability is needed to understand opportunities to bolster older adults’ socializing habits.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".