Bibliographic record
Abstract
In Classical Athens, as well as in our modern digital era, governance has been achieved through tokens. Tokens enabled voting on projects, representation, and belonging. The Distributed Autonomous Organisation (DAO) launched on the basis of cryptocurrency and blockchain technology was conceived as a form of algorithmic governance with applications in the organisation of companies. The visionaries of the DAO envisaged, among other things, a new form of sociality, which would be transparent and fair and based on a decentralised, unstoppable, public blockchain. These hopes were dashed when the DAO was exploited and drained of millions of dollars' worth of tokens within days after launching. The conversation published in the present article is conceived as an interdisciplinary discussion about the phenomenon of the Decentralised Autonomous Organisation and its impact on perceptions of sociality. Topics include the idea of the DAO as an algorithmic authority, the lessons learned when the project failed, the revolutionary beginnings of cryptocurrency technology and its potential in voting technologies, as well as the changing notions of cryptography in light of cryptocurrency technologies. Exchanges Discourse Podcast A Spoken Abstract from…Dr Mairi Gkikaki [4:38]
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".