Control and Personalization:Younger versus Older Users' Experience of Notifications
Bibliographic record
Abstract
With the increasing ubiquity of mobile technology, users are more connected than ever. Notifications facilitate prompt connections to friends, family and work, but also distract us from what we're doing. We investigated how older and younger users thought about, interacted with, and personalized their notifications. We took a qualitative approach, conducting semi-structured interviews primed through a notification categorization activity. We interviewed 20 participants with equal numbers of younger (19-30 years old) and older (48-74) adults. We extend and refine previous qualitative work and show that while enjoyment plays a minor role in the experience of notifications, urgency, directness and social closeness are far more important factors, though context remains a nuanced issue. We found that older users especially desired a sense of control over their notifications that was difficult to achieve with current technology. Lastly, we provide information about what “categories” of notifications users perceive and expand how that can be used in new personalization systems. These results lead us to advocate a number of fundamental changes to how notifications are personalized.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".