Dried fish at the intersection of food science, economy, and culture: A global survey
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it