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.
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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".