How Do I Know What I See Until I Hear What I Say?
Bibliographic record
Abstract
Abstract Whereas we acknowledge that drawing as process and product within the visual arts is complex and wide ranging, this study concentrates on testing the effectiveness of different pedagogical strategies for enhancing accuracy as a learning objective in drawing from observation. Based on our collective experience teaching observational drawing, we hypothesised that describing a scene verbally in advance of drawing it would encourage a more sophisticated appraisal of the scene and result in a more accurate representation. To test this hypothesis, drawings were made under three conditions: beginning to draw immediately, waiting before drawing, and describing the scene verbally before drawing. The drawings were then rated for accuracy by expert and non‐expert raters. Interestingly, the ‘wait’ condition had the greatest average benefit to drawing accuracy; however, the comprehensiveness of the description in the ‘describe’ condition also had a positive effect on drawing accuracy. As the number of words used in the description increased, the drawing accuracy increased such that the better descriptions result in drawings superior to the average in the ‘wait’ condition. This finding recommends that when drawing accuracy is important one should generate as rich a description as possible before beginning to draw.
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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".