Moving across drawing and sounding: Listening to trees
Bibliographic record
Abstract
Drawing and sounding both rely on bodily movement as a basis for artistic expression. Movement provides the underlying spark that brings both visual and sonic practices to life. As part of a long-term research project titled ‘Sonic Kinesthetic Forest’, this paper reflects on a collaborative exchange between three researchers who use moving, drawing and sounding practices to deepen human relationships with trees. Through this exchange, we developed embodied approaches to drawing using sound and movement prompts to unearth the essence of trees that may be less apparent through representational forms of visual expression. To support this research, our approach was applied within a university seminar called ‘Listening to Trees’ that invited landscape architecture students to tune into the actions, ephemeralities and temporalities of trees. With bodily movement as an underlying impulse, students engaged in a simultaneous performance of sound-making and charcoal drawing (sounding–drawing) to translate the hidden qualities of trees to paper. This research demonstrates that movement can stimulate a dynamic sounding–drawing process through which the sonic kinaesthetic essence of trees can be explored. As an alternative to visually focused representational or scenic views of trees and landscapes, this process aims to offer a multi-sensory mode of expression for engaging with the living world.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".