Bibliographic record
Abstract
The crypto world promises all peer-to-peer transactions disconnected from government regulations. Movement to decentralise financial institutions (DeFi), via blockchain technology, was underpinned by a belief of using smart contracts. Finance encompasses all transactions between Today Goods and tomorrow goods (TDTM). This trading world is currently under construction. According to Fisher, trading with future entails fundamental TDTM principles. According to Coase, all contracts are subjected to transaction costs. The success of DeFi ultimately must answer to how transaction costs are lowered in TDTM. The 2020 world pandemic fuelled crypto activities. If crypto can live up to its promise for reducing transaction costs, a fiat world inflation is actually a crypto world deflation. Existing real investment in the crypto world can increase or decrease the marginal product of capital, DeFi Rate is different from the real interest rate. This article interprets the findings in the framework of Fisher and Coase.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".