Survey of Living Conditions In The Arctic: What Did We Learn?
Bibliographic record
Abstract
In countries around the Arctic, tens of thousands of Iñupiat, Inuit, and other indigenous peoples live in small, isolated communities where jobs are scarce, incomes are low, and life is not easy. Yet many—including large majorities in Canada, Northern Alaska, and Greenland—are satisfied with life in their communities. That was the puzzle researchers from Statistics Greenland faced in 1994, when they studied living conditions and found that common measures of well-being—like levels of employment—didn’t explain why so many of Greenland’s Inuit chose to stay in their communities. About 7,250 Inuit, Iñupiat, and other indigenous peoples were interviewed in Greenland, Northern Alaska, the Chukotka region of Russia, and the Inuit settlement areas of Canada. The Institute of Social and Economic Research (ISER) conducted the survey in Alaska. This publication describes the survey and introduces the wealth of new information now available on the lives of the Arctic’s first people, measured in ways they themselves chose. Also printed in Valerie Moller, Denis Huschka and Alex Michalos (eds). Barometers of Quality of Life Around the Globe: How Are We Doing? New York: Springer Verlag, 107-134.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".