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
This paper examines the geographic factors that are associated with the spread of COVID-19 during the first wave in Sweden. We focus particularly on the role of place-based factors versus factors associated with the spread or diffusion of COVID-19 across places. Sweden is a useful case study to examine the interplay of these factors because it did not impose mandatory lockdowns and because there were essentially no regional differences in the pandemic policies or strategies during the first wave of COVID-19. We examine the role of place-based factors like density, age structures and different socioeconomic factors on the geographic variation of COVID-19 cases and on deaths, across both municipalities and neighborhoods. Our findings show that factors associated with diffusion matter more than place-based factors in the geographic incidence of COVID-19 in Sweden. The most significant factor of all is proximity to places with higher levels of infections. COVID-19 is also higher in places that were hit earliest in the outbreak. Of place-based factors, the geographic variation in COVID-19 is most significantly related to the presence of high-risk nursing homes, and only modestly associated with factors like density, population size, income and other socioeconomic characteristics of places.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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