Regional Patterns of Fish and Wildlife Harvests in Contemporary Alaska
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Subsistence harvests of fish and wildlife play a vital role in the economies and ways of life of rural Alaska communities. State and federal laws establish a priority for subsistence over other fishing and hunting. These laws recognize that the economic, cultural, and social role of subsistence fishing and hunting is not uniform across Alaska: federal law limits eligibility to rural residents, and state law, while allowing all state residents to participate, requires the identification of nonsubsistence areas where subsistence fishing and hunting are not permitted. But defining “rural Alaska” and “nonsubsistence areas” sparked decades of political debate and litigation. A review of nonsubsistence areas by the Alaska Joint Board of Fisheries and Game in 2013 resulted in updated estimates of noncommercial fish and wildlife harvests. Comprehensive data from systematic household surveys in 198 rural communities provided a basis for estimating harvest levels and trends at census-area and statewide levels and crucial input to board deliberations. In 2012, rural Alaska harvests averaged 134 kg/person, while urban Alaska harvests averaged 10 kg/person. The statewide rural harvest was 26% below an estimate for the 1980s, but changes varied by region. Throughout the Arctic and Subarctic, factors shaping subsistence harvests include development, the rising costs of living, shifting resource populations, regulations, climate change, and cultural change. Understanding the vulnerability and adaptability of northern communities requires monitoring of subsistence harvests through annual programs and periodic comprehensive community studies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it