Coyote Walks: A Relational and Narrative Framework for an Emergent Practice
Bibliographic record
Abstract
This practice based research is grounded in multi-day walking and camping activities guided by procedures which alter the ways I perceive and participate with my more-than-human surroundings. From these walks emerge animations, installations, oral presentations, as well as virtual and material objects which draw relations between humans, animals, plants, landscapes, and other entities: A creek visited during a walk spawns a carved series of stones, and a story about the birth of a child. The research practice can be understood as a relational network that is dispersed across time, place and medium. The network can also be read as a narrative, where an understanding of the practice becomes more complex as each object is discovered and incorporated into the larger story. The work attempts to understand the world through sensorial experience, indigenous ways of knowing, and Husserl and Merleau-Ponty’s phenomenology. From these embodied perspectives, relationality and respectfulness emerge as dominant themes in the creation of the work. This narrative-relational structure acts as a reflexive framework that guide the form and content of art objects, and gives meaning to the work in a gallery space. The utility of the framework is expanded, tested and reinforced by drawing on fellow artists including Duane Linklater’s Decommission and Valère Costes’ Tortue.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.054 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".