Interspecies Cyber-Governance: BeeDAO and the Artistic Imaginaries of Blockchain for Planetary Regeneration
Bibliographic record
Abstract
This article explores the relationship between artistic experimentation of technology and blockchain-based governance systems. It is based on a case study of the BeeDAO project. Imagining new forms of interspecies urban dwelling, the artists and activists behind BeeDAO proposed to create a blockchain-based governance model (a Distributed Autonomous Organization [DAO]) dedicated to improving the living conditions of bees. We analyze how artistic imaginaries of technology may envision new governance frameworks to shape sustainable futures. We characterize as techno-ecological agentivity the reliance on blockchain systems to design organizational models where other-than-human living entities would be integrated into the participatory governance of natural ecosystems. Our analysis highlights that BeeDAO enacts a cybernetic vision of governance, emulating a techno-determinist utopia of social transformation. The project faced practical challenges due to the intricacies of human collaboration, technological dependencies, and the complexities of urban ecologies. Yet as an artistic initiative, it significantly contributed to the urban renovation project of Haus der Statistik by using blockchain as a narration-building tool.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".