Bridging Knowledge Systems to Improve Ecosystem Management along the Yukon River: How Indigenous Peoples can Prepare Themselves for Climate
Bibliographic record
Abstract
Loss of knowledge comes in many forms and for Indigenous people means a loss of culture. This research is part of a bigger movement taking place in Indigenous communities concerned with how to salvage and create Indigenous Knowledge and traditions that are tied to culture. When we talk about “saving” knowledge such as Traditional Ecological Knowledge (TEK) this cannot be done abstractly. Instead we must consider the knowledge system as a functioning, living, and growing entity with intangible mechanisms contributing to the entire system. Indigenous Fishers Knowledge is an example of such a system and when we talk about saving IFK we are talking about preserving the methods related to traditional fishing that comes with the lived experiences over generations. This study uses interviews with Alaskan Native Fishers on the Yukon River about king salmon (Oncorhynchus tschawytscha) fishing to define Indigenous Fishers Knowledge and the contributing mechanisms that are negatively affected by policy. This also highlights how regulation of king salmon fishing interferes with intergenerational knowledge transmission that is changing how culture is communicated for future generations. As a way to move forward this research proposes implantation of a participatory research mapping method to gradually build trust between IFK holders and management entities. Indigenous knowledge systems are comprised of generations of knowledge, meaning these systems carry the memory, effort, and voice of our ancestors and to not acknowledge these systems in management systems is a disservice to all involved.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".