A Lesson for the Future: Will You Let Me Violate Your Privacy to Save Your Life?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
COVID-19 was an unprecedented pandemic that changed the lives of everyone. To handle the virus’s rapid spread, governments and big tech companies, such as Google and Apple, implemented Contact Tracing Applications (CTAs). However, the response by the public was different in each country. While some countries mandated downloading the application for their citizens, others made it optional, revealing contrasting patterns to the spread of COVID-19. In this study, in addition to investigating the privacy and security of the Canadian CTA, COVID Alert, we aim to disclose the public’s perception of these varying patterns. Additionally, if known of the results of other nations, would Canadians sacrifice their freedoms to prevent the spread of a future pandemic? Hence, a survey was conducted, gathering responses from 154 participants across Canada. Next, we questioned the participants regarding the COVID-19 pandemic and their knowledge and opinion of CTAs before presenting our findings regarding other countries. After showing our results, we then asked the participants their views of CTAs again. The arrangement of the preceding questions, the findings, and succeeding questions to identify whether Canadians’ opinions on CTAs would change, after presenting the proper evidence, were performed. Among all of our findings, there is a clear difference between before and after the findings regarding whether CTAs should be mandatory, with 34% of participants agreeing before and 56% agreeing afterward. This hints that all the public needed was information to decide whether or not to participate. In addition, this exposes the value of transparency and communication when persuading the public to collaborate. Finally, we offer three recommendations on how governments and health authorities can respond effectively in a future pandemic and increase the adoption rate for CTAs to save more lives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it