Bibliographic record
Abstract
There has been an explosion of interest globally in food security and sovereignty with the increasing awareness of the limitations and negative consequences of the agri-industrial food system on human health, physical environment and social equity and in the escalation of anthropogenic climate change. \nWithin this context, this paper focuses on how social and solidarity enterprises based on locally based food systems are addressing environmental, social and economic issues. These enterprises are meeting the need for food security and food sovereignty. This emergent social economy in the food sector is contextual and place-based and has the potential to be more resilient by building on long-established traditional practices and the protection of food crop seeds that carry the genetic diversity so critical to adaptation to unforeseen future global circumstances. These challenges to the dominant food system \nand its economic underpinnings present numerous opportunities to grow and expand the social and solidarity economy and enhance community food sovereignty and security throughout the world. The paper concludes by proposing a series of questions as a catalyst for state-civil society dialogue to develop public policies for the social and solidarity economy.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".