Delivering Value: Best Practices for Alternative Models of Sustainable Regional Food Distribution in Canada
Bibliographic record
Abstract
Canadian food producers with annual gross receipts totaling less than $1 million CAD and small to medium-sized farms find it difficult to compete with the conventional food system on price and availability (Stott et al., 2014). The lack of suitable distribution services and sales channels to urban markets (population greater than 100,000 persons) within their region has been a barrier for these producers (Hild, 2009). As a result, such producers are partially or fully excluded from the conventional supply chain and access to local food options is hampered. In the province of British Columbia, as elsewhere in Canada, local producers are challenged to meet the increase in demand for locally and sustainably produced food (Sott et al., 2014b). Challenges in managing the aggregation, marketing and distribution (purchase, storage, transportation and resale) while also scaling up production create additional costs, concern for preservation of producer identity and the potential for increased logistical complexity (Deloitte, 2013). Chefs, consumers, retailers and processors have indicated that the gap in distribution is a barrier to buying local food (Stott et al., 2014b). Analysis of the marketing and distribution activities of successful sustainably-minded regional distribution networks in British Columbia reveal shared best practices that deliver value to producer, buyer and consumer. Primary research supported the following best practices found in secondary literature: Product quality is defined by consumers and paramount to meeting purchase expectations; product differentiation adds value, increasing price premiums for all those involved in its trade; distribution & logistics supports are necessary for producers; and fair & stable pricing enables long-range sales planning and reduces switching costs.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".