The market and shadow value of informal fish catch: a framework and application to Panama
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
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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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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