Kinship, Family, and Exchange in a Labrador Inuit Community
Bibliographic record
Abstract
Kinship, family, and household have received considerable attention in Inuit studies; this paper takes a comparative social networks approach to these issues. Here kinship connections are represented in network form as a composite of individual kinship dyads of descent, coparentage, or siblingship. The composite kinship network is then used as a standard of measure for the pair-wise distances of exchange/dependency dyads appearing in other social networks within the community (including the country-food distribution network, store-bought-food-sharing network, traditional-knowledge network, alcoholco-use network, household-wellness networks, job-referrals network, and the housing network). This analysis allows us to gauge the role that kinship (of various distances, including household and family) plays in structuring exchanges across these various network domains. The data used here was collected in Nain, Labrador in January– June 2010. From 330 interviews, we extracted more than 4,900 exchanges and patterns of helping relationships among the 749 current adult residents of the community, and more than 10,000 kinship connections among a total of 1,687 individuals directly linked by descent, marriage or coparentage. The results of this analysis show that past emphasis on kin-oriented exchange in Inuit communities has mistakenly emphasized the nature of the exchange item (traditional versus store-bought (cash) economy) thereby missing important data on the nature of the exchange itself (reciprocal or one-way).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".