Managed out of existence: over-regulation of Indigenous subsistence fishing of the Yukon River
Bibliographic record
Abstract
Humans are adversely affected by the loss of vital fishery resources, specifically Indigenous peoples and the traditional knowledge systems that are foundationally tied to their culture. Discounting the ability and knowledge of Indigenous peoples stems from concepts rooted in “Tragedy of the Commons” in which a shared resource, in this case fisheries, if left unchecked will be destroyed by the mismanagement of users. The Alaskan fisheries policy regime is recognized as one of the best managed and influential fisheries in the world, but the state is predominantly driven by a conservation approach that discounts other knowledge systems. Alaskan Native fishers, for example the Gwich’in, who maintain a sustainable 30,000 year (conservatively) relationship with their environment, possess culturally specific Traditional Ecological Knowledge (TEK) that is a result of the mechanisms or physical act of fishing, thus giving meaning to the term we will use in this paper, Indigenous Fishers’ Knowledge (IFK). Alaskan Natives along the Yukon River derive specific knowledge about their environment and King Salmon through the act of fishing. TEK is not static nor is IFK as it is transmitted to younger generations through the practice of fishing. TEK and IFK play a large role in the transmission and acquisition of knowledge, they both connect knowledge to culture and play a role in creating culture and traditions, they are in fact very intricate systems. Indigenous fishers seek inclusion and involvement that does not separate them from their knowledge but recognizes and implements their practices/control on a local level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".