To erase or not to erase, that is not the question: Drawing from observation in an analogue or digital environment
Bibliographic record
Abstract
Erasing when drawing occurs for a variety of reasons. While the most obvious may be correction of mistakes, at other times erasers are used to create such things as highlights or marks that introduce particular aesthetic elements. When a drawing is made on paper, partial erasure ‘marks’ can provide a useful record of a drawing’s evolution. For the teacher, this historical record can be a catalyst for helpful commentary and criticism. While programmed to simulate an analogue eraser, in a digital environment the erase function can eradicate a drawing’s history with a single click. We studied analogue and digital tool use behaviours (including erasing) to compare the frequency of erasure and the effect of erasing on observational accuracy in adults between the age of 17 and 64 with various levels of drawing experience from less than two years to more than ten years. The study involved participants making one drawing on paper with traditional drawing tools and one drawing on a digital drawing tablet. We then had the drawings rated for accuracy. Among other interesting results, we found that erasing occurs with greater frequency when participants work in a digital environment than in an analogue one and that, while there were significant tool use differences between the environments, those differences did not result in differences in the accuracy of final drawings indicating the adaptability of our participants using different means to achieve the same effect.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".