Raising Funds with Smart Contracts: New Opportunities and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Among recent FinTech developments, new digital ledger technologies have the potential to facilitate the financing of entrepreneurial projects, as they can enable different and better financing contracts. Costly verification is arguably one of the main reasons why bank financing and debt contracts have been traditionally so prevalent, with investors not being easily assured that entrepreneurs will report accurately future cash flows generated. The adoption of digital ledger technologies can mitigate this friction, by offering a better tool to maintain a shareable history of transactions, which not only reduces verification costs but also further enables “smart contracts” which can benefit from adjusting optimally to incoming data. Such smart contracts (the optimal form of which is found to be a dynamically adjusting profit-sharing rule) dominate less flexible debt and equity contracts that do not give the right incentives for the entrepreneur to continue to try to generate sales, especially when there is learning from data. There remain unresolved issues around digital ledger technology, especially with “proof-of-work” systems, which create limitations for realizing its potential. Permissioned systems may solve some of these problems but remain at an experimental stage. Third-party platforms that collect and share information are another way to reduce the verification costs faced by individual investors, and there seems to be a close link between the evolution toward “smart” contracts and crowdfunding. The appropriate supporting regulation still needs to be established and will have to tackle issues that are quite novel compared to what banking regulations and securities markets regulations have had to address.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it