DNA Transnational Data Journeys and the Construction of Categories of Suspicion
Bibliographic record
Abstract
Background Systems for large-scale data exchanges are playing a pivotal role in the governance, surveillance, and social control of criminality in different parts of the world. Analysis This article explores the case study of the Prüm system, which is a technological system for the exchange of DNA data among several European Union (EU) countries. Making use of the concept of data journeys, it addresses how the transnational exchange of DNA data in the EU implicates the construction of categories of suspicion. Conclusion and implications The article shows how supranational- and national-level notions and attitudes over the ownership of data shape data journeys, and it discusses the societal implications of datafication and emerging data justice issues.
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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 | 0.022 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.017 | 0.074 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".