Urban harvests: food security and local fish and shellfish in Southcentral Alaska
Bibliographic record
Abstract
Alaska is known for its many fisheries, which support an extensive global marketplace, a thriving tourism industry, and also contribute much to diets of many Alaskans. Yet, some research has suggested that Alaska’s food security has been impacted negatively by the development of export-oriented commercial fisheries and tourism-oriented sport fisheries. In this paper, we discuss two sets of interviews that we completed with participants in two food fisheries in the Kenai Peninsula region of Southcentral Alaska: sockeye dipnet fishing and razor clam digging. We encountered a great deal of cultural and socioeconomic diversity among the participants of each, though a far greater proportion of the clam fishery were Alaska Native than in the salmon fishery. In both fisheries, people report participating both as a matter of food security and family tradition. Likewise, participants in both fisheries reported a great deal of experience with and knowledge of the fisheries. Many clam diggers worried that the fishery was being overharvested, despite the apparent abundance of clams that year, and this proved prescient to the fishery’s closure 2 years later. In the salmon fishery, some people were similarly concerned about the sustainability of the fisheries. Ultimately, our paper provides a descriptive account of participants in these two fisheries and sheds light on how important wild food harvests can be to the food security of Alaska’s urban residents. We recommend that future resource management policies continue to support the role of fisheries in local food security.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".