COVID-19 in Europe: Dataset at a sub-national level
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic has hit humanity, straining health care systems, economies, and governments worldwide. In one of the responses to the pandemic, a big global effort has been mounted to collect, analyze, and make data publicly available. However, many of the existing COVID-19 public datasets are (i) aggregated at country level, and (ii) tend not to bring the COVID-19-specific data coupled with socio-demographic, economic, public policy, health, pollution and environmental factors, all of which may be key elements to study the transmission of the SARS-CoV-2 and its severity. To aid the evaluation of the determinants and impact of the COVID-19 pandemic at a large scale, we present here a new dataset with socio-demographic, economic, public policy, health, pollution and environmental factors for the European Union at the small regions level (NUTS3). The database is freely accessible at http://dx.doi.org/10.17632/2ghxnrkr9p.4. This dataset can help to monitor the COVID-19 mortality and infections at the sub-national level and enable analysis that may inform future policymaking.
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.
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 | 0.002 | 0.136 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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