Bibliographic record
Abstract
Over the last 70 years, Canadian agriculture has shifted from many small farms that supplied local residents, to fewer large farms designed to maximize production, reduce cost, and target international markets. At present, small local food chains exist as a small fraction of the Canadian food system. However, during the COVID-19 pandemic, the role of local producers was valued by Canadians. The purpose of this study was to gain insight into the role that local producers played in maintaining food system resilience during the early part of the COVID-19 pandemic. We were particularly interested in identifying adaptation strategies and factors that contributed to (enabled) or worked against (constrained) increasing local food system resilience (i.e. the perseverance of farms and farm production). We also examined the accessibility and sufficiency of current agriculture supports. Eight semi-structured interviews were conducted with Antigonish Farmers’ Market (AFM) producers. Challenges identified include system bottlenecks, increased costs, increased demand, changes in sales, and the need for online literacy. In response to these challenges, AFM producers demonstrated a high degree of adaptability. Half of the study participants accessed agriculture-support related to COVID-19. Other participants expressed discontent with the suitability and accessibility of current support programs available. Opportunities to increase local food system resilience include increasing local support, promoting AFM collaboration, and tailoring agriculture support for small, diversified, local farmers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".