Where to Begin? Eye-Movement When Drawing
Bibliographic record
Abstract
For over a century, drawing from observation, at least at the introductory level, has been integral to many secondary and most post-secondary art school programs in Europe and North America. Its place in such programs is understood to develop an ability to see and interpret on a flat surface the real, three-dimensional world; this skill, in turn, provides support to related mental processes such as memory, visualization, and imagination. Where an artist looks when drawing from observation may not be arbitrary and can be observed, quantified, and analyzed. Our interest in examining the first few minutes of the drawing process takes its lead from the novice’s question, "Where should I begin?" Attempting to understand these first few minutes led to a collaborative study between art educators and cognitive-perceptual psychologists: the former interested in implications for practical pedagogy, the latter in applying expertise in eye movement and scientific methodology in service of a specific real-world question. The stated purpose of the study notwithstanding, contrasting histories and practices in art and science provided contexts for discussion beyond the collection and interpretation of data. This article seeks to report upon and further that discussion.
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 | 0.002 | 0.022 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".