Resilient niches: how non-financial capitals helped to overcome the COVID-19 crisis in local food systems
Bibliographic record
Abstract
The COVID-19 pandemic created new challenges for actors in food systems. In many regions it threatened food security, well-being, and local economies. Its dynamic poses new kinds of problems and questions about sustainability, food sovereignty, and resilience of food systems. This paper focuses on the specificities of those actors in the system that are deprived of financial capital and are economically vulnerable but nevertheless manifest unique flexibility and develop diverse resilience strategies. Small, local, and informal food networks possess specific non-material resources, which shape their reaction to crisis. The results of a qualitative analysis of three case studies from Krakow (Poland) suggest that small-scale, informal structures, direct communication channels, and a pro-innovation environment together with a strong focus on social values are conducive to formation of resilient responses to the crisis. Soft capitals (relational, organizational, and knowledge capital) should therefore not be treated as a compensation for shortages of financial capital, but as important and effective resources for creating sustainable food 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".