Social economy of food initiatives that are nourishing communities through “power-with” practices
Bibliographic record
Abstract
From 2014 to 2019, Nourishing Communities: Sustainable Local Food Systems Research Group explored food initiatives in the social economy, many of which use practices like bartering, gifting, and self-provisioning, that remain under-recognized for their economic value. Nourishing Communities considered how these organizations may contribute to food security, community development, and environmental remediation, especially for marginalized groups. Its researchers collaborated with such organizations to complete participatory action projects and a range of products to communicate the initiatives’ impacts. As three of those researchers, we subsequently synthesized the material from these outputs to show the resources, barriers, and impacts of the respective initiatives. This meta-analysis reveals that these initiatives not only produce economic, social, and environmental benefits, but also work to organize human relations. Beyond considering how initiatives in the social economy of food interact with the market economy, we use Karlberg’s schema of power to illustrate their potential to reconceptualize human relations. Here we find them gravitating towards “power with” practices that emphasize cooperation over competition. Throughout, we employ the concept of framing to propose ways in which that re-conceptualization might “grow legs” and extend further into larger social discourses. In so doing, we find the initiatives strategically invoking alternative framings of work, knowledge, social relations, and value in order to explain the impact of their own work. Although further research is needed regarding the meaning that impact and power hold for social economy initiatives, this research contributes to scholarly debates surrounding the potential of food initiatives in the social economy.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".