Community-driven food networks as vehicles of rural social innovation
Bibliographic record
Abstract
Background: Rural communities in Cape Breton, Canada, face persistent challenges such as food insecurity, social isolation, and economic marginalization. Community-led food networks rooted in social economy values have emerged as innovative responses that provide dignified, culturally relevant access to food. This article’s novelty lies in its comparative, community-engaged analysis of an Indigenous and a non-Indigenous rural model, treating food networks as infrastructures of rural social innovation rather than charity. Research objectives: This study explored how community-driven food networks contribute to rural development and social innovation by fostering inclusion, empowerment, and resilience. Research design and methods: Using a qualitative case study approach, the research examined two food networks in rural Cape Breton through document analysis, community feedback, observation, and interviews with key stakeholders. Results: The findings reveal that integrated programming, i.e., combining food access, wellness, and employment initiatives, enhances social cohesion, local capacity, and community dignity. Conclusions: Community food networks exemplify how social economy initiatives can transform rural spaces into hubs of innovation and care.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".