PubCast - Alternative Seafood Networks During COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Export-oriented seafood trade faltered during the early months of the COVID-19 pandemic. In contrast, alternative seafood networks (ASNs) that distribute seafood through local and direct marketing channels were identified as a “bright spot”. In this paper, we draw on multiple lines of quantitative and qualitative evidence to show that ASNs experienced a temporary pandemic “bump” in both the United States and Canada in the wake of supply chain disruptions and government mandated social protections. We use a systemic resilience framework to analyze the factors that enabled ASNs to be resilient during the pandemic as well as challenges. The contrast between ASNs and the broader seafood system during COVID-19 raises important questions about the role that local and regional food systems may play during crises and highlights the need for functional diversity in supply chains. This paper was authored by Joshua S Stoll, Hannah L Harrison, Emily De Sousa, Debra Callaway, Melissa Collier, Kelly Harrell, Buck Jones, Jordyn Kastlunger, Emma Kramer, Steve Kurian, M Alan Lovewell, Sonia Strobel, Tracy Sylvester, Brett Tolley, Andrea Tomlinson, Easton R White, Talia Young, Philip A Loring. This paper was originally published in Frontiers in Sustainable Food Systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 it