Assessing the pocket market model for growing the local food movement: A case study of Metropolitan Vancouver
Bibliographic record
Abstract
In this study we explore the pocket market model, an emergent alternative retail marketing arrangement for connecting urban consumers with local food producers. In this model, community-based organizations act as local food brokers, purchasing fresh, healthful food from area farmers and food producers, and selling it to urban consumers in small-scale, portable, local food markets. The benefits of pocket markets are numerous. They include the provision of additional and more localized marketing outlets for local food producers; increased opportunities to educate consumers about local food and sustainable food systems; the convenience for consumers of having additional venues where local food is available for purchase; and an ability to increase access to fresh produce in areas with poor or limited retail food options. Despite these advantages, pocket market organizers face many challenges in implementing this model successfully. These include a lack of public familiarity with the pocket market concept, an inability to address issues of food access in a way that is financially sustainable, and issues related to logistics, site selection, and regulatory requirements. In this paper, we will explore the pocket market model using those operating in metropolitan Vancouver (British Columbia, Canada) as an example, and assess the degree to which it addresses some of the current gaps in bringing local food to urban communities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".