Working Together to Build Cooperative Food Systems
Bibliographic record
Abstract
First paragraphs: The combined challenges of food insecurity, agriculture-related environmental decline, corporate concentration, and the decline of community resilience are being met by growing societal interest in developing more just and sustainable food systems. A recent emphasis on cooperation and innovative forms of collective action within the food movement invokes a community-centered approach to food provisioning where collective problem-solving and democracy take center place in the development agenda (Ikerd, 2012). Cooperative alternative food networks are becoming powerful tools for community development and important vehicles for cultivating democratically controlled food systems at multiple scales. The papers in this special issue provide an important contribution to our understanding of the function, the challenges, and the potential of collective action in enabling more just and resilient food systems. Cooperative alternative food networks represent a break from the competitive productivism of the dominant food economy and create new relational spaces that hold promise for overcoming the pragmatic and political limits of some of the more individualistic approaches in the local/ sustainable food movement. These include cooperative forms of: food hubs, local food networks, farmers' markets, CSAs, box schemes, buying clubs, and value chains, along with a range of agriculture and food cooperatives owned by farmers, consumers, workers, and in emerging multistakeholder cooperative structures. With a renewed emphasis on civic governance, the resulting food-provisioning systems are based on principles of participatory democracy, solidarity, and reciprocity (Renting, Schermer, & Rossi, 2012) and provide spaces to nurture collective subjectivities required for transformative food practice and politics (Levkoe, 2011)....
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".