Bibliographic record
Abstract
During the coronavirus disease 2019 (COVID-19) pandemic, data regarding new infections were commonly presented and used to guide policy decisions (e.g., whether to close schools) and personal choices (e.g., whether to dine at a restaurant). In this manuscript, we highlight a critical aspect of pandemic data that can pose a challenge for people trying to reason about it. Data on infections-like much time series data-can be presented as either stocks (the total number of cases) or flows (the number of new cases over some interval). We show that seeing the same data presented in one format versus the other can shift judgments of risk and behavioral intentions. Specifically, when participants were shown data that depicted the number of new cases each day (flow) decreasing, they judged the current risk of COVID-19 to be lower than participants who were shown the same data as the total (cumulative) number of cases (stock), which-by its nature-continued to increase. Risk appraisal, in turn, predicted a wide array of behavioral intentions (e.g., likelihood of dining indoors at a restaurant). Thus, the choice of how to present pandemic data can lead people to different conclusions about risk and can have practical consequences for risky behavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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.
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 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".