COVID-19, digital health technology and the politics of the unprecedented
Bibliographic record
Abstract
The COVID-19 global pandemic has stretched the capacities of public health institutions and health systems around the world, opening the door to a range of technologically-driven solutions. In this article, we seek to historicize the expanding role of digital health technologies and examine the political-economic context from which they have emerged. Drawing on critical insights from science and technology studies, we maintain that the rise of digital health technologies has been catalyzed by broad shifts in global health governance that have expanded the role of market forces in public health and a unique set of political and economic crises that have accelerated the adoption of digital technologies—often under the guise of appeals to technological innovation to address “unprecedented” crises. These interrelated historical trends, we contend, are critical for understanding current state responses to the pandemic and possibilities for more equitable and democratic applications of technology in public health.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".