Understanding a Revolutionary and Flawed Grand Experiment in Blockchain
Bibliographic record
Abstract
In spring 2016, the Distributed Autonomous Organization (The DAO) was created on Ethereum. As with Bitcoin, Ethereum uses a P2P network, where distributed ledgers are implemented as daisy-chained blocks of data. Ethereum's native cryptocurrency, Ethers are spent to execute pieces of code called smart contracts. Investors paid their Ethers for the DAO to operate and received the opportunity to vote on and become investors in venture projects proposed by Ethereum-based startups. Transactions and settlements between investors and startups are executed autonomously. The DAO experiment failed shortly after inception as an anonymous hacker stole over $50M USD worth of Ethers out of the $168M invested. The Ethereum community voted to return (or fork) the state of the network to one prior to the hack, returning Ethers back to investors and shuttering the DAO. However, this action arguably represented as a bailout—ironically, Bitcoin was conceived as a reaction against the 2008 bailout of US banks—and violated the ledger immutability and “code is law” ethos of the blockchain community.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".