www.e3network.org Socioeconomic Indicators for Fisheries: A Case Study of the Yukon River Salmon Fishery
Bibliographic record
Abstract
Sustainable fisheries, by definition, should include environmental, economic and social considerations, yet the use of economic and social indicators to date has been limited, both from a management and consumer perspective. While a number of studies to date have focused on the development of these types of indicators, fewer have tested their application. This study seeks to describe broadly relevant social and economic indicators, specifically focused on human communities associated with fisheries resources. It also seeks to assess whether the indicators proposed can readily be populated using existing, publically available, data sources. To that end, we conduct a case study analysis of the Yukon River commercial salmon fishery. Our findings suggest that the majority of the indicators proposed can be populated with existing data, often already collected on an annual basis. Recognizing the case study nature of this paper, we also assess the availability of relevant data for commercial salmonoid fisheries in other regions. Moving from individual indicators to the idea of how these indicators could be used to create a method for assessing the sustainability of a fishery more broadly (so as to include economic and social considerations), we suggest a two part assessment, including both required and voluntary standards associated with various socioeconomic indicators. From a management perspective, integration and tracking of such indicators along with environmental/biological ones will likely improve ecosystem-based management in which humans are also a key factor.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".