Effect of Arrangement of Stick Figures on Estimates of Proportion in Risk Graphics
Bibliographic record
Abstract
BACKGROUND: Health risks are sometimes illustrated with stick figures, with a certain proportion colored to indicate they are affected by the disease. Perception of these graphics may be affected by whether the affected stick figures are scattered randomly throughout the group or arranged in a block. OBJECTIVE: . To assess the effects of stick-figure arrangement on first impressions of estimates of proportion, under a 10-s deadline. DESIGN: . Questionnaire. Participants and Setting. Respondents recruited online (n = 100) or in waiting rooms at an urban hospital (n = 65). Intervention. Participants were asked to estimate the proportion represented in 6 unlabeled graphics, half randomly arranged and half sequentially arranged. Measurements. Estimated proportions. RESULTS: . Although average estimates were fairly good, the variability of estimates was high. Overestimates of random graphics were larger than overestimates of sequential ones, except when the proportion was near 50%; variability was also higher with random graphics. Although the average inaccuracy was modest, it was large enough that more than one quarter of respondents confused 2 graphics depicting proportions that differed by 11 percentage points. Low numeracy and educational level were associated with inaccuracy. Limitations. Participants estimated proportions but did not report perceived risk. CONCLUSIONS: . Randomly arranged arrays of stick figures should be used with care because viewers' ability to estimate the proportion in these graphics is so poor that moderate differences between risks may not be visible. In addition, random arrangements may create an initial impression that proportions, especially large ones, are larger than they are.
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 | 0.015 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".