From Data Universalism to Data Particularism: Epistemologizing Digital Sovereignty Based on Germany's and Japan's COVID-19 Responses
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This article examines the use of digital technologies by Germany and Japan during the COVID-19 pandemic from the perspective of two divergent epistemologies of data, “data universalism” and “data particularism.” Data universalism is grounded in large-scale, context-free data aggregation. In contrast, data particularism interprets data in context, factoring in spatial, cultural, and institutional particularities. In line with WHO recommendations, Germany focused on widespread PCR testing and used case incidence as an indicator for imposing comprehensive lockdowns. Japan, on the other hand, adopted a context-specific approach that focused on detecting and preventing infection clusters, combined with flexible and targeted measures that took into account material spaces and cultural characteristics. Despite the Japanese approach's lower public approval in the first two years of the pandemic and despite its failure to avert an overload of the health system—which the German approach did achieve—, Japan outperformed Germany in terms of selected key metrics at the end of the COVID-19 pandemic. We argue that, beyond COVID-19, data particularism, as exemplified in Japan's COVID-19 response, can be a valuable strategy to address the epistemic and political shortcomings of data universalism. In particular, we argue that in the context of the broader debate about digital sovereignty, data particularism is attractive for traditional manufacturing powers, such as Japan and Germany, that are rich in context-specific data but dependent on US and Chinese Big Tech. We describe the turn towards different epistemologies of data as an epistemologization of the digital sovereignty debate.
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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 | 0.002 |
| 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 | 0.003 | 0.003 |
| 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