Embodied Kinesthetic Arts Practices with Arboreal Kin
Bibliographic record
Abstract
Trees and forests are an important component of human and ecological health yet human destruction of forests continues at an accelerated rate. This research investigates embodied art practices as a means to shift human and tree relations from extraction to kinship. The investigation dances into the possibility that trees could be collaborators in an art-making process. The portfolio includes ten short video poems developed through a four-stage methodology of listening, tracing, translating and presenting movement investigations with trees. The portfolio is presented with reflections on the development of the work, field notes, contextual references and includes an informal artist’s talkback. The portfolio includes a lesson plan for group investigations into human and tree relationships and an outline for group investigations with fellow artists. The potential of dance practices to develop kinship bonds with trees was revealed by the art-making process and demonstrates the capacity of arts-based methodologies to shift our human epistemologies and ontologies. Further research into this and other arts methodologies, particularly collaborative and improvisational approaches, could be of huge benefit in adapting to climate change and supporting multi-species relational shifts.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".