Digital location tracking of children and adolescents: a theoretical framework and review
Bibliographic record
Abstract
Many parents in the U.S. have begun using GPS-based digital location tracking (DLT) technologies (smartphones, tags, wearables) to track the whereabouts of children and adolescents. The paper lays the foundation for an emerging science of DLT by performing the first-ever theoretical analysis and review of empirical literature on DLT. First, we develop a framework to clarify how DLT should be conceptualized and measured, how it compares to historical strategies for monitoring youths’ location, and the mechanisms by which it might affect youth adjustment. Second, we review what is known about DLT today, finding that (1) DLT use is now common from childhood to emerging adulthood, with 33-69% of families using it; (2) there are sociodemographic differences in DLT use; (3) DLT use has significant cross-sectional associations with other parenting behaviors, with family functioning, and with youth adjustment; and (4) there is much speculation, but minimal data, about the new ethical and developmental issues that might arise from DLT use (e.g., privacy invasions). Third, we critique the existing evidence base to outline priorities for future research, emphasizing the need for longitudinal data, better measurement, and moving beyond convenience samples. We conclude that DLT is a new, common, and vastly understudied parenting behavior of clinical and developmental relevance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".