Dot Plots: A Useful Alternative to Bar Charts
Bibliographic record
Abstract
Readers make a number of judgments when reading graphs: they may judge the length of a line, the area of a wedge of a circle, the position of a point along a common scale, the slope of a line, or a number of other attributes of the points, lines, and bars that are plotted. Cleveland and McGill (1984) identified tasks or judgments that are performed when reading graphs and conducted carefully designed experiments to determine which of these judgments we make most accurately. They then designed a graph to take advantage of the knowledge gained from their experimentation. The result was the dot plot. This article introduces the dot plot and offers before and after examples to compare presentations using bar charts and dot plots. The dot plot in Figure 1 shows the revenues of the top 60 companies from the Fortune 1000 list. Figure 2 shows these same revenues using a bar chart. Most readers would have little problem understanding either the dot plot or the bar chart. Note that the dot plot is less cluttered, less redundant, and uses less ink. Figure 1: This dot plot shows the revenues of the top 60 companies from the Fortune 1000 list.
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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 | 0.011 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.072 | 0.017 |
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".