Logarithmic vs. Linear Visualizations of COVID-19 Cases Do Not Affect Citizens’ Support for Confinement
Bibliographic record
Abstract
In public health crises, the media and governments routinely share statistical analyses with the public. In the COVID-19 pandemic, the tool most commonly used to convey statistical information about the spread of the virus has been time-series graphs about the cumulative number of cases. When drawing such graphs, analysts have to make design decisions which can have dramatic effects on citizens’ interpretations. Plotting the COVID-19 progression on a linear scale highlights an exponential “explosion” in the number of cases, whereas plotting the number of cases on a logarithmic scale produces a line with a modest-looking slope. Even if the two graphs display the exact same information, differences in visual design may lead people to different substantive conclusions. In this study, we measure the causal effect of different visualization design choices on Canadians’ views about the crisis. We report results from a survey experiment conducted in April 2020 with a sample of 2500 respondents. We find that no matter how the information is presented, Canadians are united in supporting drastic confinement measures and in accepting that these measures will not be removed soon.
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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 | 0.004 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".