Data Performativity and Health: The Politics of Health Data Practices in Europe
Bibliographic record
Abstract
The European Commission produces the European Core Health Indicators (ECHI), a database containing different tools used to compare European Union (EU) countries and recommend policy changes. The ECHI feeds multiple reports and documents and finds its way into health policies. From this arises the main research question addressed in this paper: How is health in Europe influenced by ECHI data practices? Specifically, we look at how some health issues or populations are prioritized or dismissed, which ultimately shapes the meaning of and knowledge about health in Europe. To do so, we first develop the conceptual framework of “data performativity,” underlining how data practices shape their object/subject. We then explore the politics of evidence behind the ECHI health data that materialize into (1) the absence of some health issues and populations and (2) the hypervisibility of neoliberal health. In the end, we argue, the ECHI serves as a site of individual, collective, and political identity enunciation.
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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 | 0.218 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.011 | 0.110 |
| Scholarly communication | 0.046 | 0.029 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".