New forms: Anthropocene Festivals and experimental environmental governance
Bibliographic record
Abstract
This paper is about the “Anthropocene Festival,” a concept we develop to explore proliferating and multi-faceted arts-based events, happenings, unconferences, and workshops which collectively model novel forms of environmental governance. The Anthropocene Festival mobilizes disruptive and creative possibilities at the juncture of digital and ecological life, while simultaneously embodying developments in the institutional form of green capitalism. To argue these points, we locate the Anthropocene Festival within a proliferation of new institutional environmentalisms, including biennales, hackathons, and initiatives in the neoliberal university. Next, we provide a survey of recent examples, observing across them an increasingly hegemonic template of environmental sociality—or model of collective interaction—rooted in digital technologies. Next, we discuss two examples of environmental governance propositions expressive of the Anthropocene Festival ethos: (1) Climate Symphony, a project that uses sonification techniques to facilitate new understandings of climate change, (2) Terra0, an art project which reconceptualizes forest ecology and non-human agency using blockchain technology. We conclude by arguing that the ontological generativity of the Anthropocene Festival arises from the dissenting approaches to conventional models of environmental governance it cultivates, but that the Anthropocene Festival does not necessarily carry a radical political valence because of this.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".