Drawing as Language: Celebrating the Work of Bob Steele
Bibliographic record
Abstract
Drawing as Language: Celebrating the Work of Bob Steele is a Festschrift in honour of Bob Steele, Professor Emeritus, artist, educator and tireless advocate for bringing authentic aesthetic lived experiences to young children. Bob Steele’s prolific contribution to the field of visual arts education recognizes the importance of drawing for everyone, but especially with young children. As an artist-teacher-researcher Bob has devoted decades to developing understandings of drawing as language. He is a progressive thinker with commitment and passion, and through a lifetime of work has provoked serious engagement with children’s drawing processes: how children learn through drawing, through authentic experiences with their sensory world, and through their intense engagement with stories. In this unique collection we have invited educators and scholars whose work represents the ongoing influence of the ideas and teachings of Bob Steele: what he has brought to the field of art education, early childhood studies, and curriculum studies in general. It traces the history and development of his ideas. The reader is taken through his journey as a young educator in rural Saskatchewan, Canada to significant moments in his teaching and his work. The voices of the contributors offer an insightful alternative into how drawing need not be limited to a particular discipline but can be language of communication; a language that significantly matters in the daily lives and learning not just only for children, but for those who also work with them. We hope this Festschrift inspires you to think about the drawings of children differently and take your understanding to a new level.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".