Bibliographic record
Abstract
For policy-makers and managers, knowing what information to collect is just as important as collecting information. I apply economics-based methods, including the value of information approach, to natural resource management in order to identify new optimal policies and priority areas for investment. Explicitly incorporating uncertainty is key to these methods, both in formally acknowledging alternative hypothesis and strategies, and for selecting policies that are most robust to uncertainty about natural and social systems. Given their differences in objectives and current challenges, I develop and apply methods to both developing and developed marine fisheries. In Mexico, for example, I estimate that total fish catch over the last fifty years could be almost twice that reported in official data. This ‘informal’ catch reduces economic benefits from fisheries output, including informal processing and sales that add less value to production. Based on current monitoring investment and informal catch rates, I estimate that this represents an almost US$1 billion annual loss in foregone economic impacts, that could be partially gained by an annual investment of US$100 million to increase formalization of current catch. The benefits of assessing information value are not limited to developing fisheries or “data-poor” contexts. Linking ecosystem models with economic data and frameworks, I estimate that the supporting service value of forage fishes as food for other fished species vastly outweighs their yearly landed value (in the Southern Baja California Peninsula, US$180 million compared to US$62 million). For the California Current, which includes Mexico, the US and Canada, I couple game-theoretic and ecosystem models and find that moving beyond single-species valuation supports arguments for sustainable fishing of forage fishes, and creates incentives for cooperative fishing strategies across a range of climate scenarios. Aside from developing new and broadly applicable methods and frameworks, the overarching finding of this work is that it is always beneficial to formally and openly acknowledge uncertainty and alternative management strategies in natural resource assessments. This allows us to provide robust advice to policy-makers given, and not stymied by, uncertainty.
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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 | 0.026 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.025 | 0.042 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".