Bibliographic record
Abstract
The COVID-19 pandemic is a disruption which has posed challenges to food sector organizations. Yet it may also provide an opportunity for growth of and change in these organizations. Our aims were to describe the surge in demand and innovations introduced by a rural food distribution cooperative and to critically assess responses in light of organizational resilience literature. We chose an organizational case study approach with mixed methods. Data sources included: existing reports, newsletters, policies and quantitative indicators; and new stakeholder interviews (n=20). We describe: the development and nature of the cooperative; its consolidation and anticipatory planning; the March 2020 surge in orders (133/week in February to 205/wk in March); the prioritization of health and safety in modifying product receiving and delivery methods; the warehouse reorganization and product aggregation doubling to twice per week; the strains on employees and human resource challenges; yet the growing organizational resilience. We reflect on the small role of the cooperative in the inequitable agri-food system of the counties it serves, yet the strong role it plays with other food security oriented organizations in keep with its values. Further work, both research and practice development, can continue to explore the ways in which complex multi-stakeholder, not-for-profit, socially and environmentally principle food organizations can better navigate disruptions in the coming years, particularly in rural areas.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".