Multiple Pie Charts: Unreadable, Inefficient, and Over-Used
Bibliographic record
Abstract
A pie chart is a common way of presenting simple statistics—for example, the amount of time devoted to teaching, researching, and administration by the faculty can be illustrated by dividing a circle (‘pie’) into three appropriately sized segments. There has been much discussion about the strengths and weaknesses of such pie charts for a long time, and it is not likely to end soon. 3D pie charts show the same statistics, but in three dimensions. Multiple pie charts can be found where the data for groups to be compared are presented in adjacent pies. Here we argue that, even if a simple pie chart may have some advantages, the same cannot be said for multiple pie charts. Multiple pie charts are difficult to analyze and interpret, especially when comparing adjacent pies.
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 | 0.077 | 0.403 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".