Visualizing the Geographic and Demographic Distribution of COVID-19
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
Whereas African Americans are disproportionately among the coronavirus disease 2019 (COVID-19) pandemic’s sick and dead, less is known about whether some racial/ethnic groups are more likely to be affected in Canada. In this data visualization, the authors address two issues limiting understanding of the spatial and demographic distribution of the COVID-19 pandemic in Canada: (1) COVID-19 infection and death counts are collected at a very high level of geographic aggregation, and (2) these counts are not tallied by sociodemographic group, including race/ethnicity. The authors use a bivariate choropleth map to illustrate the correlation between COVID-19 infections and the percentage of residents who are Black across census subdivisions. Canada is more similar to the United States than expected in this respect: areas with higher shares of Black Canadians also see more infections.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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