Using Blockchain to Create Transaction Identity for Persons Experiencing Homelessness in America: Policy Proposal
Bibliographic record
Abstract
More than 500,000 people experience homelessness in America each day. Local and federal solutions to the problem have had limited success because of the fragmentation of services and lack of valid and timely information. Billions of dollars spent to provide reliable, timely, and actionable information in health care have exposed the difficulty of establishing such a system using the prevalent information technology solutions. However, relying on successful examples of the use of blockchain to help refugee populations and poor farmers internationally, we have partnered to propose an innovative solution to this problem using the case of people experiencing homelessness in Austin, Texas. This paper aims to describe one of the first applications of blockchain technology for addressing homelessness in the United States by creating a digital identity for people experiencing homelessness and engaging emergency medical services and clinical providers. The authors argue that a lack of documentation to prove personal identity and the inability to access own records are major hurdles for empowering persons experiencing homelessness to be resilient and overcome the life challenges they face. Furthermore, it is argued that this lack of information causes misdiagnosis, duplication, and fragmentation in service delivery, which can be potentially addressed by blockchain technology. Further planning for creating a program on the ground with additional funding will demonstrate the results of using blockchain technology to establish digital identity for persons experiencing homelessness.
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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 | 0.032 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.017 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".