Towards ethical surveillance of smartphone use among youth: exploratory digital citizen science approaches shaping the understanding of ubiquitous technology use
Bibliographic record
Abstract
Smartphones are widely used among youth, yet no standardized measures accurately capture smartphone use. This exploratory study utilized the Smart Platform, a digital citizen science initiative, to collect objective, real-time data on smartphone use among youth and investigate differences between retrospective and objective measurements. Youth self-reported smartphone use retrospectively via a validated questionnaire, while objective use (i.e., duration and day of use) was captured over one week using a custom-built app. Differences between the two measures were assessed using Wilcoxon signed-rank test and linear mixed effects models and visualized with Bland-Altman plots. A total of 85 participants with 257 observations (mean age=15.65, SD=1.68) were included in this study. Retrospective smartphone use was higher than objective use in the overall sample (4.074 vs 2.615 hours/day, p=0.019), on weekdays (3.973 vs. 2.714 hours/day, p=0.024) and on weekends (4.026 vs. 2.323 hours/day, p=0.014). The mean difference between the measures was 1.39 hours/day (95% CI [-6.99, 9.76]), with larger differences at higher use levels. Mixed effects models confirmed the difference between retrospective and objective use (β=1.341, C.I. [0.637, 2.071], p<0.001), adjusting for sociodemographic factors. Females also reported higher use than males (β=1.027, C.I. [0.068, 1.985], p=0.045). Accurate measurement of smartphone use is imperative to understand its impacts among youth. Privacy and security challenges associated with objective measures can be addressed through digital citizen science, where youth have the power to share, withdraw, or delete their data.
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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 | 0.060 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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 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".