POLICY IMPLICATIONS OF MANAGING BIODIVERSITY AND NATURAL RESOURCES ACROSS INTERNATIONAL BOUNDARIES
Bibliographic record
Abstract
Fisheries Management under the best of scenarios is a complex action. It requires thoughtful consideration of resources that tend to be out of sight, widely distributed, highly variable both spatially and temporally, and present dramatic variation in life history and ecology. No one management approach has been developed which can effectively incorporate all these variables. Add to this the issue of transnational boundary movements of these resources, and one discovers that this complex issue needs to be addressed by multiple entities, agencies, and nations to have any chance of success. This research set out to discover ways in which fisheries management could be improved across transnational boundaries. With a multi-tiered approach, using interviews, surveys, and literature review, I discovered the state of cooperative management on transnational fisheries management in the populations of Lake Trout (a success) and Atlantic Cod (a failure) that occur in the United States and Canada as case studies. Fishery management decisions were not being guided by the life histories of fish, stakeholders are generally well informed on fisheries actions that are occurring across borders, and there is a lack of commitment from governments to make sacrifices to reduce overfishing. Ultimately, fisheries management is people management because politics, socioeconomics, public perceptions, as well as available science must all be considered. Data from this research then provides rationale for a series of recommendations for policy action which can broadly be applied to further improve transnational fisheries management into the future so that we can reliably reproduce the success of trout management and avoid the failures of cod management. The lessons learned, and policy prescriptions, should be transferable to co-management of other transnational fisheries populations across international borders.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".