Publishing with Tree-Media: Arbo-real Aesthetics, Pedagogical Ruptures
Bibliographic record
Abstract
In response to the ecological and epistemic crises of the Capitalocene, this paper examines how eco-artist Thijs Biersteker develops tree-media—sensor-driven AI installations that treat trees and fungi not as metaphors or data sources, but as co-authors of environmental knowledge. Through the concept of arbo-real aesthetics, the paper proposes an elemental model of publishing rooted in multispecies reciprocity, latency, and refusal. Biersteker’s installations resist extractive AI paradigms by staging alternative epistemologies grounded in vegetal sensing, seasonal rhythms, and symbiotic time. Analyzing six installations produced between 2018 and 2024, the paper theorizes how these works enact a compostable media logic—one that unsettles mastery and reimagines publishing as a sensory, ethical, and relational process. Rather than offering techno-utopian solutions, the installations inhabit the Promethean paradox: they critique digital extractivism while operating within its constraints. As a prescriptive intervention, the paper introduces Listening with Trees, a three-day pedagogical prototype that speculatively translates these insights into multispecies publishing practices. By publishing with trees—through slowness, decay, and co-authorship—this model offers a low-carbon, speculative alternative to academic and AI-driven knowledge systems in the age of the Chthulucene.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".