Bibliographic record
Abstract
This paper describes a household survey of Inuit in northern Alaska and how the survey data were used to better understand the relative importance of jobs, wild food harvesting, and social ties for life satisfaction. It emphasizes the importance of non-material measures for life satisfaction. It builds on other research showing the importance of harvesting wild food and the persistence of a mixed economy—one that combines cash income and wild food harvests. An empirical model estimates the relationship between people's choices to work, and/or hunt and fish, and individual satisfaction with life. The model includes economic and non-economic measures of well-being as well as community characteristics and shows that what matters most for satisfaction are family ties, social support and opportunities to do things with other people. Jobs, income, housing, and modern amenities—are less important among arctic Inuit. This research addresses the purpose for the original survey project—to give a more realistic picture of life in the Arctic by showing why people who live in remote, isolated, communities, with low incomes, and substandard housing are very satisfied with their lives. It also contributes to public policy in remote regions and efforts to understand how people are adapting in a rapidly changing environment.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".