Towards a Truly Decentralized Blockchain Framework for Remittance
Bibliographic record
Abstract
Blockchain is a revolutionary technology that is constructively transforming many traditional industries, including financial services. Blockchain demonstrates immense potential in bringing substantial benefits to the remittance industry. Although the remittance industry has crossed the mark of USD 600 billion in 2021, remittance cost is still substantially high, around 6% on average, indirectly limiting financial inclusion and promoting de-risking. The involvement of multiple intermediaries in global remittances makes cross-border payments more expensive. Many projects, including Ripple and Stellar, employ blockchain technology to provide alternative infrastructure for cross-border payments. However, the decentralization of blockchain networks in both solutions is debatable. This paper examines the market characteristics impacting remittance cost, a prominent factor driving the evolution of the remittance industry. A truly decentralized blockchain framework viz. LayerOneX, which provides remittance services at a reduced cost, is proposed in this paper. Devices with low computation and memory capacity can act as transaction validators in this solution. A universal wallet across homogeneous and heterogeneous blockchains is proposed to facilitate fast and inexpensive remittance services. Thus, a novel framework for true decentralization of blockchain-based remittance services, resulting in reduced cost and, therefore, better financial inclusion, is proposed in this paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".