Letting the solution define the problem: Canada’s COVID Alert app as a case of failed policy
Bibliographic record
Abstract
Early in the global COVID-19 pandemic, smartphone-based apps were touted as a means to mitigate and even end the pandemic. The Canadian government debuted its contact notification app, the COVID Alert app, on 29 July 2020. As elsewhere, the app failed to live up to even modest expectations before being quietly retired on 17 June 2022. The COVID Alert app suffered an embrace of technological solutionism – defining a problem in terms of a preferred solution – and dataism, ‘the presumption that social reality can be fully captured by the collection of digital data’. Taken together, these ideologies short-circuited the policymaking process to focus more on the tech itself and the needs of the tech giants providing the digital infrastructure – Apple and Google – than on the nominal health-policy objective. In the end, failure was inevitable.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.033 | 0.019 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".