Colour-Enhanced Star Plot Glyphs: Can Salient Shape Characteristics Be Overcome?
Bibliographic record
Abstract
This article reports two experiments addressing the question of how the shape characteristics of star plot glyphs influence classification tasks and whether additional graphic features such as colour can be used to counterbalance the effects of shape characteristics. In a previous study we found that salient shapes of star plot glyphs, such as “has one spike,” influence the classification of the data represented by the glyphs. The shape differences in star plot glyphs are induced by assigning variables to rays in different ways. The first two experiments showed shape influences; we then conducted two follow-up studies to shed more light on the influence of shape. First, to address the question of how the classification of star plot glyphs would be affected if they were stripped of their meaning, participants were asked to group star plot glyphs as shapes. The second study colour-coded the rays of star plot glyphs to focus attention on differences in salient shapes; for example, the general shape characteristic “has one spike” cannot be applied so easily if each spike is a different colour. The results show that colour-enhancing star plot glyphs improves processing speed and reduces the influence of salient shape characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".