Evaluating the Costs and Benefits of a Smart Contract Blockchain Framework for Credit Default Swaps
Bibliographic record
Abstract
Despite wide speculation about its use-value, there are very few large-scale Blockchain implementations, particularly in sophisticated financial applications and mature markets. The extent of Blockchain’s disruptive potential in these domains is uncertain. This Article considers Blockchain’s use-value for credit default swap contract execution, fulfillment, and post-trade processing by using, as an assessment base, a series of derivative industry whitepapers, academic and technological evaluative studies, and commentary relating to current market undertakings. In summary, when applied to credit default swaps, there are many barriers to implementation, as well as costs, fragmentation risks, technological deficiencies, and practical drawbacks. As a result, there is some doubt on the extent of Blockchain’s short-term transformational value for complex financial structures and mature trading markets. This, at least in part, explains the fact that Blockchain projects are currently slow to materialize in derivatives and other financial market applications.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".