Exploring the inter-connections between Alternative Agrifood and Seafood Networks for building food systems resilience
Bibliographic record
Abstract
In the context of intensifying threats to food systems and a growing need for resilience, Alternative Agrifood Networks (AANs) and Alternative Seafood Networks (ASNs) have emerged as notable bright spots across North America. Collectively, AANs and ASNs comprise Alternative Food Networks (AFNs) - the micro, small, and medium-sized enterprises which are important, but often overlooked, actors in food systems. However, a critical limitation for food system resilience is that agriculture and fisheries remain chronically siloed in research, legislation, regulation, and advocacy. In this field report, we explore the opportunities and challenges of linking ASNs and AANs to build more resilient food systems. To do so, we draw on our experiences as an interdisciplinary group of food systems researchers and practitioners that came together in 2022 through the Agrifish Resilience project. Based on a series of reflective collaborative conversations that we held as a team, we share our key insights for building resilience across agriculture and fisheries focusing on three main themes: the role of ASNs and AANs in food system resilience, our perspectives on what resilience in food systems means, and prospects for collaboratively building resilience. We conclude by proposing the idea of boundary objects as a way of bringing ASNs and AANs together, with some examples of what this looks like in practice, and the role for interdisciplinary teams like ours.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.034 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".