The market and shadow value of informal fish catch: a framework and application to Panama
Bibliographic record
Abstract
Fisheries catches are known to be widely underreported, and much of their value flows in informal markets. Goods and services that are not directly sold in a market also have a corresponding economic value, here termed ‘shadow value’, which can apply to discarded fish—or those that are consumed but not sold (e.g., subsistence catches). Here, we estimate the monetary value of fisheries catches in Panama that are landed but not reported, or that are discarded at sea; this includes catches from artisanal and industrial fleets, as well as recreational and subsistence fisheries. Based on available data, we estimate that the market and shadow value of unreported catches in Panama in 2010 was around US$92 million, equal to approximately 43% of the total reported landed value. In the case of discarded fish, the shadow value represents the potential but entirely unrealized economic benefit of landing such fish; in the case of unreported landings, unreported market value represents only the first link in the potentially sophisticated informal seafood economy. One must be careful in considering these results for policy. It is possible that, rather than seeking to capture these ‘lost’ benefits, fish that are discarded or unreported should not have been caught at all, for example, if they are juveniles or of threatened species; conversely, unreported subsistence catches are crucial for food security throughout the world. These results help contextualize the scale of unreported fisheries in economic terms, and can inform subsequent policies and strategies to ensure social, ecological, and economic sustainability.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".