Bridging Gaps: A Framework for Developing Regional Food Systems
Bibliographic record
Abstract
Local food research has been generally focused on strengthening the alternative food system by scaling up local agriculture, rather than advancing strategies to bridge gaps between local farmers and conventional food retail businesses. Competitive advantage theory forms the foundation of a framework based on Porter’s (1985) firm (business unit) value chain for investigating food system gaps, and a logic model for promoting development by adding value throughout the alternative food supply chain. In the present study, a survey created jointly by local stakeholders investigated factors that food retail businesses consider when sourcing local food. Among the top rated factors, supporting the local economy (opportunity) and regular delivery (barrier) were seen as significant to the regional food system of the Algoma District in central Canada. Mapping these factors through the firm value chain framework revealed a high degree of interconnectedness to other factors in the survey, including importance of obtaining fresh food, consistency of supply throughout the year, and reducing overall costs of supplying affordable products. Analysis of the survey results from the perspective of a food retail business pointed to information technology and coordinated distribution methods as playing important roles in adding value to the regional food system. In addition to these results, the downtown of the study site has emerged as an aggregation point for local food, and local food may be playing a role in revitalizing the downtown. The value chain framework analysis can be applied to other localities to bridge gaps between local farmers and conventional supply chain actors.
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".