Dried fish at the intersection of food science, economy, and culture: A global survey
Bibliographic record
Abstract
Abstract Dried fish—here defined broadly as aquatic animals preserved using simple techniques, such as sun‐drying, salting, fermentation, and smoking that permit storage as foods at ambient temperature for extended periods without specialized packaging—have received little direct attention in fisheries research. This lack of visibility belies their historical and contemporary importance. Prior to the introduction of refrigeration, dried fish were the main form in which fisheries catches were traded and consumed. Dried fish products remain a core component of production, trade, diets, and cuisines across the world, particularly in the Global South. The dried fish sector provides employment for millions of people, particularly women, who comprise most of the fish‐drying workforce in many locations. However, the sector also confronts and creates significant challenges including food safety concerns and exploitative labour conditions. This paper is the first systematic assessment of the global literature on dried fish, comprised of a sample of >1100 references. In contrast to the general fisheries literature, which is dominated by studies of ecology and governance and focusses mainly on primary production, the dried fish literature is dominated by studies from food science and concentrates on the processing segment of fish value chains. As such, it offers valuable reference point for fisheries research, which is becoming increasingly attentive to food systems. This paper uncovers a wealth of insights buried in this largely unheralded literature, and identifies key thematic intersections, gaps and research questions that remain to be addressed in the study of dried fish.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".