Sustaining Tribal Fisheries: U.S. Economic Relief Policies during COVID-19
Bibliographic record
Abstract
This article reviews the individual spend plans of U.S. states granted a funding allocation under Sec. 12005 of the Coronavirus Aid Relief and Economic Security (CARES) Act to identify consistency with legislative mandates to support Tribal commercial, subsistence, cultural, or ceremonial fisheries negatively impacted by the COVID-19 pandemic. Utilizing critical discourse analysis, this study identifies state discursive practices in supporting Tribal sovereignty in fisheries management for the advancement of Indigenous Ocean justice. State spending plans (n = 22) publicly available and submitted to the National Oceanic and Atmospheric Administration before July 2021 were reviewed. Few of the state spend plans listed impacts to Tribal fisheries due to the pandemic. Only two state plans included Tribal consultation and direct economic relief for commercial, subsistence, cultural, and/or ceremonial losses faced by neighboring Tribes and Tribal citizens. Overall, the protections within the CARES Act for Tribal fisheries were not integrated into state spend plans. The article identifies best practices for state fisheries relief policy content that is affirming of Tribal fishing rights and uses them to help address the ongoing pandemic crisis facing Tribal fisheries. These findings have relevance for future emergency relief programs that are inclusive of Tribal Nations. Honoring Tribal sovereignty and the federal trust responsibility must be the cornerstone of shared sustainable fisheries.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".