Household Harvesting, State Policy, and Migration: Evidence from the Survey of Living Conditions in the Arctic
Bibliographic record
Abstract
Household harvesting of wild fish and game contributes to food security in indigenous communities across the Arctic, and in some regions plays an important role in cultural identity of indigenous peoples. The degree to which the state regulates harvesting and restricts distribution of country foods varies widely, however, and this intervention in local economies can affect livelihood opportunities. The paper hypothesizes that where state policy has contributed to harvesting remaining a culturally embedded livelihood strategy, its contribution to the quality of life may influence people to remain in rural communities, despite potentially lower material living standards. Lacking such a cultural linkage, harvesting may become the employer of last resort for people unable to find paying jobs or leave declining communities for a better life elsewhere. The paper examines the association between Survey of Living Conditions in the Arctic (SLiCA) respondents’ intent to remain in their community of residence and household harvesting, cash income from work, and other relevant factors. The results include both similarities and differences for residents of arctic Alaska, arctic Canada, Greenland, and Chukotka. Systematic differences found appear consistent with the hypothesis about the role of household harvesting and state policy toward harvest and distribution of country foods.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".