Alternative Currency Movements as a Challenge to Globalisation?: A Case Study of Manchester's Local Currency Networks
Bibliographic record
Abstract
Over the past 15 years, local money networks, which are essentially trading networks using a community-created currency, have emerged in countries as far apart as Argentina, Australia and New Zealand, Canada and the US, continental Europe and Japan. They range from Local Exchange Trading Schemes (UK), to Time Dollars (US), Green Dollars (New Zealand, Australia and Canada), Trading Circles (Hungary), Barter Networks (Argentina) and Talents (Germany). Drawing on an ethnographic case study of alternative currency movements in Manchester, UK, this book provides an analysis of the motivations, aims, successes and failures of alternative currency networks. It also raises questions such as the contribution of the alternative currency movement to current debates about alternatives to neoliberalism. While it is theoretically informed, critical and grounded in fieldwork, it is also sympathetic to the political aims of the protagonists and cognisant of the non-economic benefits that arise from their development.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".