Virtual barriers: unpacking the sustainability implications of online food spaces and the Yellowknife Farmers Market’s response to COVID-19
Bibliographic record
Abstract
BACKGROUND: Through their support of local agriculture, relationships, and healthy diets, farmers markets can contribute to a sustainable food system. Markets like the Yellowknife Farmers Market (YKFM) are social spaces that support local food, yet the COVID-19 pandemic has forced changes to their current model. We explore the potential of online marketplaces to contribute to a resilient, sustainable food system through a case study of the YKFM. METHODS: In 2019, a collaborative mixed-method evaluation was initiated by the YKFM and university partners in the Northwest Territories (NWT), Canada. The evaluation included an in-person Rapid Market Assessment dot survey and questionnaire of market patrons from two YKFM dates prior to the pandemic. Due to COVID-19, a vendor survey and interviews were deferred. Data collected from the two patron surveys, alongside researcher observations, available literature, public announcements, and informal email and phone discussions, inform the discussion. RESULTS: For the patron surveys, 59 dot survey and 31 questionnaire participants were recruited. The top motivators for attendance were eating dinner, atmosphere, and supporting local businesses, and most patrons attended as couples and spent over half of their time talking to others. The YKFM did not move online; instead, they proposed and implemented a "Shop, don't stop" market. Informal conversations suggested the small scale of the market and technology challenges were perceived barriers to moving online. The physically-distanced market was well-attended and featured in local media. CONCLUSIONS: NWT food strategies rely on farmers markets to nurture a local food system. Data suggest a potential incongruence between an online model and important market characteristics such as the event-like atmosphere. Available literature suggests online markets can support local food by facilitating purchasing and knowledge-sharing, yet they do not replicate the open-air or social experience. The decision not to move online for the YKFM reflects market patron characteristics and current food context in Yellowknife and the NWT. While online adaptation does not fit into the YKFM plan today, online markets may prove useful as a complementary strategy for future emerging stressors to enhance the resiliency of local systems.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".