Reflective Spring Cleaning: Using Personal Informatics to Support Infrequent Notification Personalization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Distracting mobile notifications are a high-profile problem but previous research suggests notification management tools are underused because of the barriers users face in relation to the perceived benefits. We posit that users might be more motivated to personalize if they could view contextual data for how personalizations would have impacted their recent notifications. We propose the ‘Reflective Spring Cleaning’ approach to support notification management through infrequent personalization with visualization of collected notification data. To simplify and contextualize key trends in a user’s notifications, we framed these visualizations within a novel who-what-when data abstraction. We evaluated it through a four-week longitudinal study: 21 participants logged their notifications before and after a personalization session that included suggestions for notification management contextualized against visualizations of their recent notifications. A debriefing interview described their new experience after two more weeks of logging. Our approach encouraged users to critically reflect on their notifications, which frequently inspired them to personalize and improved the experience of the majority.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it