Blockchain for Migrants: Promoting Self-Sovereign Identity and Financial Inclusion
Bibliographic record
Abstract
This article explores the potential of Distributed Ledger Technology (DLT), with a focus on blockchain, to address key challenges related to the security, ownership, and management of personal data. We trace the foundational work of Haber and Stornetta, who introduced the core principles of blockchain to secure digital records within the real economy. Building on this, Nakamoto’s innovations in blockchain technology introduced a native crypto-asset, which not only aligns and concentrates the interests of network participants but also resolves the previously unsolved “double-spending problem.” This breakthrough decentralizes the verification and control of recorded information, enhancing security in monetary transactions.Migrants often face challenges related to rights protection, identity management, and limited access to financial services. Blockchain applications, with their strengths in secure data storage, transparent transactions, and reliable identity verification, offer promising solutions. In this article, we examine real-world blockchain applications that enhance identity management and foster financial inclusion for migrants. Blockchain provides an infrastructure that empowers individuals with greater control over their financial and personal data, particularly through self-sovereign identity (SSI) and the use of stablecoins as global currencies. These innovations are becoming foundational components of a new digital ecosystem for information and finance.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".