Immeasurable sovereignty: Indigenous well-being, fishery science, and sustainable governance
Bibliographic record
Abstract
Well-being and equity are increasingly identified as integral to environmental governance and improved sustainability outcomes. Greater consideration of these dimensions has generated calls for more data and new methodologies capable of collecting, evaluating, and converting social and cultural data into formats deemed more useful to decision makers. These efforts expose gaps and challenges related to an over reliance on quantitative data, especially when it comes to adequately accounting for the well-being of Indigenous communities. Located along the western shore of Nanvarpak (Lake Iliamna) in southwest Alaska, this paper examines Indigenous conceptions of well-being and provides insights on how to better account for the well-being of Indigenous communities in sustainable governance. Carried out in partnership with the Tribal Nation of Igyaraq (Igiugig), we draw on ethnographic and interview data to identify and examine three foundational elements of Indigenous well-being: (1) land relations or nunaka (my land, my birthplace), inclusive of one’s responsibility to ensure continuation of a way of life defined by connections to ancestral lands; (2) sovereignty; and (3) effective governance. We pay special attention to the implications of Indigenous well-being as primarily expressed and achieved through enactments of sovereignty and nation-building. We draw attention to the need for greater investment in diverse scientific expertise and data but caution against assuming that more science will lead to better governance. There is a need to acknowledge the ways in which dominant Western science-policy structures do not serve Indigenous communities. Our research suggests that you cannot adequately account for Indigenous well-being without explicit consideration of governance, and the often taken for granted value assumptions and political conditions that quietly frame policy debates and scientific understandings of what data are considered useful and what impacts are considered acceptable. This paper demonstrates the fundamental importance of centering sovereignty in not only well-being and equity considerations, but as a central tenet of ethical scientific inquiry and environmental governance more broadly.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".