Complementary Currencies for Humanitarian Aid
Bibliographic record
Abstract
The humanitarian sector has gone through a major shift toward injection of cash into vulnerable communities as its core modality. On this trajectory toward direct currency injection, something new has happened: namely the empowerment of communities to create their own local currencies, a tool known as Complementary Currency systems. This study mobilizes the concepts of endogenous regional development, import substitution and local market linkages as elaborated by Albert Hirschman and Jane Jacobs, to analyze the impact of a group of Complementary Currencies instituted by Grassroots Economics Foundation and the Red Cross in Kenya. The paper discusses humanitarian Cash and Voucher Assistance programs and compares them to a Complementary Currency system using Grassroots Economics as a case study. Transaction histories recorded on a blockchain and network visualizations show the ability of these Complementary Currencies to create diverse production capacity, dense local supply chains, and data for measuring the impact of humanitarian currency transfers. Since Complementary Currency systems prioritize both cooperation and localization, the paper argues that Complementary Currencies should become one of the tools in the Cash and Voucher Assistance toolbox.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".