Researching the Teaching of Drawing [PDF, E-Book]
Bibliographic record
Abstract
The Drawing Laboratory at NSCAD University was founded with funding from the Social Sciences and Humanities Research Council of Canada in 2005 as a collaboration between psychological scientists from Dalhousie and drawing instructors at NSAD. The Drawing Lab is thus a unique place where scientists and artists collaborate on interdisciplinary research about the complex intellectual and practical act of drawing from observation. By bringing the scientific method to bear on how drawing processes unfold, those involved seek to improve drawing education while furthering research on the cognitive processes involved in drawing. The chapters in this book describe that research. ‘Perceptual and Cognitive Processes in Drawing from Observation’ will hold much interest for drawing instructors and students, psychologists and neuroscientists with a specialism in art, as well as those with a general interest in art and science. Authors of this volume are Amanda Burk, John Christie, Tim Fedak, Raymond Klein, Geniva Liu, Bryan Maycock, Mathew Reichertz and Jack Wong.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".