Globalism, localism and blue food systems – How can cross-scale tensions be reduced? A production perspective from UK seafood stakeholders
Bibliographic record
Abstract
‘Blue foods’ are derived from aquatic species that are caught or cultivated and are amongst the most globally traded commodities. Growing emphasis is being placed on blue foods in future transitions to sustainable food systems. However, recent international events such as the Covid-19 pandemic, UK-EU ‘Brexit’, and Russia-Ukraine war have caused renewed interest in ideological debates between globalism and localism and associated cross-scale tensions. Here, we aim to provide further insight into cross-scale tensions in blue food systems through a literature review and UK-based seafood stakeholder workshop. From our literature review, the evidence for cross-scale tensions was linked to the following themes: economic efficiency versus social justice; food security and food sovereignty; sustainability and traceability. A stakeholder workshop revealed a need for international market actors to support improvements in the social and environmental practices of blue food producers. Fully traceable supply chains that enable the transfer of information across jurisdictions were also deemed desirable to provide greater assurance on product legality and provenance. Developing a blue foods strategy at a national level was suggested as a solution to potentially rebalance the influence of international markets, with the objective of rejuvenating re-localised blue food systems. To be successful, a blue foods strategy will require the alignment and integration of policies that impact on different industry subsectors.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".