Community Currency in the United States: The Social Environments in Which it Emerges and Survives
Bibliographic record
Abstract
Community currency originated as a means to empower the economically marginalized. This paper studies the US population of community currency systems using locally printed money. Eighty-two systems are identified that have been attempted in the United States since 1991. Internet searches and contact with system coordinators indicate that only 20.7% of all systems are active. Regions in which they occur are described; more than one quarter are in Pacific states. City-level Census 2000 data are employed in analyses of environmental conduciveness to determine in which types of social environments local currencies emerge and survive within. Social movement theory is engaged to identify general, population-based resources for local movements. Economic marginality and labor-market-independence hypotheses are also formulated and tested. The major findings indicate that cities with local currencies are characterized by populations with lower household incomes, higher poverty rates, higher unemployment rates, and larger self-employment sectors. Evidence is also presented indicating that community currencies tend to survive in places with younger populations, higher educational attainment, fewer married people, and less residential stability. Implications concerning the future of the community currency movement and its ability to empower the marginalized are drawn.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".