Methodological nationalism and labour justice in seafood supply chains
Bibliographic record
Abstract
Abstract Drawing on the seafood industry in Thailand as our point of departure, we argue that scholarship and advocacy in seafood supply chains have often been limited by inaccurate characterisations of the diverse ways that these supply chains are organised. Scholars and labour justice advocates often assume that seafood exports from Thailand and elsewhere are produced by the domestic fishing industry, rather than accounting for the way that most raw materials are imported from non-Thai fisheries that also employ transnational migrant workers. They also assume an undifferentiated national seafood production industry. This has left labour advocacy vulnerable to counter-campaigns based on more accurate accounts of seafood supply chains, including that launched by the National Fishing Association of Thailand during the past year. We explain these inaccuracies as partly a result of methodological nationalism and territorial trap thinking. This refers to analytical frameworks that orient researchers to take the nation-state and its territorial boundaries as the main unit of analysis, while neglecting transnational networks and internal differentiation. Additional reasons include a lack of transparency and complexity in seafood supply chains, and the way that transnational advocacy networks are organised so that links across global South producing countries are weak. We illustrate an expanded supply chain approach by conducting an analysis of the labour justice issues for seafood supply chains based in and passing through Thailand.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| 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".