Preconditions for Covid-19 mobile apps: A feature-level investigation of user acceptance based on insights from South Korea and Canada, applied in the Netherlands
Bibliographic record
Abstract
Our study will reveal preferences and opinions about the adoption of Covid-19 tracking apps in the Netherlands. Evidence suggests that citizens in South Korea are more accepting of actions taken to promote public health and more open to surveillance. We expect their existing technologies, as well as experiences from Canada, to be useful in providing the basis for our survey and for discussing how to explain and market a Covid-19 app in the Netherlands. RESEARCH QUESTION: How does the Dutch public perceive the privacy and efficacy of potential technologies used for managing Covid-19? How can culturally embedded views on privacy and transparency from other countries be leveraged to enable a secure and efficient app in the Netherlands? How do experts consider the results of the survey in regards to implementation in the app? URGENCY: The Dutch government has installed a task force working on understanding technical requirements and citizen attitudes regarding Covid-19 apps. Tracking is important to prevent or manage a second wave of infections. This project aims to contribute detailed insights in just over 4 months. HYPOTHESIS: Our study will reveal preferences and opinions about the adoption of apps in the Netherlands. Evidence suggests that citizens in South Korea are more accepting of actions taken to promote public health and more open to surveillance, which has allowed for the rapid implementation of mobile apps. We expect their existing technologies, as well as experiences from Canada, to be useful in providing the basis for our survey and for discussing how to explain and market a Covid-19 app in the Netherlands. Contact Ashley Metz for access to the 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".