Polygon-Based Drawing Accuracy Analysis and Positive/Negative Space
Bibliographic record
Abstract
The study of drawing generally depends on ratings by human critics and self-reported expertise of the drawers. To complement those approaches, we developed an objective continuous performance-based measure of drawing accuracy. This measure represents drawings as sets of landmark points and analyses features of particular research interest by comparing polygons of those features’ landmark points with their counterpart polygons in a veridical image. This approach produces local accuracy measures (for each polygon), a global accuracy measure (the mean across several polygons), and four distinct properties of a polygon for analysis: its size, its position, its orientation and the proportionality of its shape. We briefly describe the method and its potential research applications in drawing education and visual perception, then apply it to a specific research question: Are we more accurate when drawing in the so-called ‘positive space’ (or figure)? In a polygon-based accuracy analysis of 34 representational drawings, expert drawers outperformed less experienced participants on overall accuracy and every dimension of polygon error. Comparing polygons in the positive and negative space revealed an apparent trade-off on the different dimensions of polygon error. People were more accurate at proportionality and position in the positive space than in the negative space, but more accurate at orientation in the negative space. The contribution is the use of an objective, performance-based analysis of geometric deformations to study the accuracy of drawings at different levels of organization, here, in the positive and negative space.
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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 | 0.005 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".