Children and Adults' Perception of Signal Colours, Symbols, and Words in the Context of Cybersecurity Warnings
Bibliographic record
Abstract
Research has shown that online security warnings are frequently ignored or misinterpreted by even experienced adult users.Children may be particularly vulnerable because they are not always aware of the risks associated with online threats.Existing work relating to cybersecurity warnings has been done with adults and there are few recommendations for children.We explore this research gap through two user studies with 22 children aged 10-12 years old and with 22 adults.We compare children and adults' perception of warning design parameters (signal colours, symbols, and words) in the context of cybersecurity warnings.Our findings suggest that while there are many similarities in how both groups interpret the signal items, differences exist which should be taken into consideration when designing for children.We adapt existing warning design guidelines by Bauer et al. to provide recommendations for warnings that effectively communicate risk to children.First, I would like to give my thanks to my wonderful supervisor, Sonia Chiasson, for her guidance, patience, and support throughout my journey at Carleton.I am forever grateful for your mentorship and am truly
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".