‘Unleashing Local Capital’: scaling cooperative local investing practices
Bibliographic record
Abstract
The excessive power of global finance capital and financial markets contributes to social inequality and ecological unsustainability. This challenge is being increasingly addressed at a community level, in innovative forms of cooperative investing that are widening membership and increasing the capital pool, while putting financial democracy, community well-being, the environment, and local business creation ahead of personal gain. A key question that warrants further scrutiny is, how can we imagine and enact a transition to an alternative financial ecosystem by scaling the impact of these financial social innovations? In this chapter we describe the “Unleashing Local Capital” (ULC) program, a cooperative local investment innovation designed by the Alberta Community and Cooperative Association, a community development organization based in the Canadian province of Alberta. The ULC project provides rural communities with a financial tool with which to retain local capital and invest in community businesses. We analyze the ULC team’s attempts to scale this cooperative investment model across Alberta and the challenges that were encountered. To do so, we applied a framework that is informed by both social practice theories and the multi-level perspective common in the study of socio-technical transitions. The most notable forces influencing scale include the incumbent financial structure and legislative systems and people’s habitual practices with respect to investing and borrowing. We discuss in detail the pivotal factors, such as the ways in which we commonly discuss money, concerns around transparency, the speed of the investment, cooperative practices, and perception of risk and trust.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".