Gaming Privacy: a Canadian case study of a children’s co-created privacy literacy game
Bibliographic record
Abstract
This paper reports the process and outcomes of the design of a game that educates children about management of privacy online. Using a participatory action research process, children worked with the researchers to develop and play a game which simulates certain aspects of online privacy management and allows for scaffolded experiential learning in a safe environment. The game allows children to develop autonomous skills and understandings, not only for more effective learning but also because it is only through autonomy that children can develop a sense of self which is necessary for understanding what it means to be private. The paper shows that children have quite sophisticated understandings of privacy, compared with some adult perceptions, and that these understandings include awareness of the risks posed by commercial organisations seeking to gather personal data from them. The paper shows how engaging children as research and design participants can lead to more successful approaches in the development of privacy literacy.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".