Contact-tracing apps and alienation in the age of COVID-19
Bibliographic record
Abstract
Using a core idea of critical social theory, alienation, we interrogate the failure in the design and adoption of a Stop-COVID app in France. We analyse the political and scientific discourse, to develop an understanding of the conditions giving rise to this failure in this unprecedented moment. We argue that the digital-first solutionist approach taken by the government failed because, as in all Western countries, most stakeholders were alienated from the reality of the COVID-19 pandemic and lacked concrete knowledge of it. Furthermore, the French government and its COVID-19 council excluded relevant scientific experts in favour of quantitative modelling based on abstract partial knowledge. This along with coercion and lack of transparency about the app, reinforced alienation, undermined effectiveness in managing the crisis and resulted in the digital design failure. We suggest that such alienation will prevail in the COVID-19 era characterised by regimes of control, rampant abusive location tracking, and data collection, and where public officials are more concerned with managing effects than seeking causal explanations. The digital-first solutionist approach was adopted, not because digital solutions (to contact tracing) are superior to traditional ones, but by default due to alienation and lack of interdisciplinary cooperation.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 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".