Digital Assets and Blockchain: Hackable, Fraudulent, or Just Misunderstood?<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT Unhackable. Immutable. Fraud‐proof. These terms are frequently used to describe cryptocurrencies and the blockchain technology that underpins them. Together, they imply that a high degree of safety accompanies cryptocurrencies and blockchain ledgers. But is this understanding supported by the facts, or is it more based on the promise and theoretical construction of blockchain and cryptocurrencies? To better answer this question, we have compiled and analyzed existing research on initial coin offerings, security offerings, blockchain hacks and thefts, and data breaches of blockchain‐based platforms and digital wallets. In contrast to the popular press, we find that in practice, blockchain and cryptocurrencies are more prone to malfeasance, fraud, and manipulation than is commonly understood. The security and trust provided by blockchain as a technology tool are only as secure as the underlying code that establishes the blockchain, and the value derived from cryptocurrencies is only as trustworthy as the entity developing the cryptocurrency. Neither are without their vulnerabilities. Skepticism and proper due diligence should be maintained for any entity looking to utilize blockchain technology or invest in cryptocurrencies.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".