Corporate contact tracing as a pandemic response
Bibliographic record
Abstract
Since the start of the COVID-19 pandemic, a steady stream of propositions from tech giants and start-ups alike has furnished us with the idea that GPS- or Bluetooth-enabled contact tracing apps are a vital part of the pandemic response. This commentary considers these apps as ‘corporate contact tracing’, emphasizing the private-sector role that such developments imply. We first discuss corporate contact tracing’s potential to de-center the power of public health authorities. Then, using the frames of surveillance capitalism and disaster capitalism, we suggest how corporate contact tracing might feed the rise of corporate power in the public sphere. We question its capacity to address structural inequalities and to foster a social justice vision of public health. And, we wonder whether corporate contact tracing might intensify the effects of discriminatory design and algorithmic oppression. We conclude by calling for a discussion of this technology beyond questions of privacy and efficacy.
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.
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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".