Smartphones, apps and older people's interests
Bibliographic record
Abstract
It is well-documented that ICT are designed mostly with young users in mind. In addition, most studies about smartphone use do not include older people or even consider age differences. Consequently, little is known about how to design smartphone apps taking older people's interests into account. We have used a mixed-method approach with an intergenerational perspective to approach this topic. First, we track the smartphone activities of 238 panelists. Second, we conduct an online survey (382 respondents). Third, we document the experiences of a group of older people in a smartphone learning club. We have found specific media consumption and communication patterns among older individuals: for example, at home they are more prone to jumping between devices for ergonomic reasons, thus, cross-device interactions are key for this group. We discuss the relevance of intergenerational studies in counterbalancing the spread of age stereotypes and identifying alternative adoption trends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".