Mapping COVID-19 in Context: Promoting a Proportionate Perspective on the Pandemic
Bibliographic record
Abstract
The novel coronavirus SARS-CoV-2 took a firm grip on human life in the year 2020. The global spread of the virus and the impacts of the associated disease COVID-19 are being tracked by numerous institutions, experts, and lay people. Thematic maps are widely used to visualize the many available metrics, including case counts, hospitalization rates, and fatalities. Despite coordination efforts at different jurisdictional levels (including global), data collection is partially inconsistent, delayed, or unfocused, and maps may exacerbate the issues of the underlying data. Numerous published maps also conflict with established cartographic guidelines and include design choices that exaggerate the spread of the coronavirus and the threat of COVID-19. This article highlights some of these issues and illustrates alternative representations that keep the pandemic in proportion. The distinction between using maps for data exploration and answering specific questions is examined, and the challenges to mapping the pandemic are related to standards of professional ethics in the GIS field.
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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 | 0.033 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".