Bibliographic record
Abstract
Research about children's safety tracking systems primarily concentrate on the hardware design of monitoring children's location and physical fitness.However, limitations remain in addressing the children's outdoor safety risks and the usability of the systems.This thesis aims to fill these gaps by surveying parents to understand their concerns about children's outdoor activities and the need for children's tracking systems.Based on parents' responses, we identified key safety risks in children's outdoor activities from traffic, stranger harassment and abduction.Therefore, we created a safety tracking system with a parental app and a wearable device for children.We conducted an expert usability evaluation to assess the prototypes' usability and alignment with parents' needs and children's cognitive abilities.Based on the evaluation, we refined the system to improve the usability of the prototype, with the goal of reducing parental anxiety and enhancing children's safety in outdoor environments.I am also sincerely grateful to the members of the CHORUS Lab for their strong support, insightful discussions, and assistance during the various stages of my thesis preparation.Their contributions were vital to the development of my research.I appreciate Dr. Fraser Taylor and Dr. Romola Vasantha Thumbadoo for their constructive comments and thoughtful advice, which greatly enriched my graduate studies.Finally, I extend my deepest gratitude to my family for their unwavering support, patience
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".