Why Wage Earners Hunt: Food Sharing, Social Structure, and Influence in an Arctic Mixed Economy
Bibliographic record
Abstract
Food sharing has been a central focus of research in human behavioral ecology and anthropology more broadly. Studies of food sharing have typically focused on either the individual’s motivations to share or the social formations and value systems that sharing produces. Here, we employ social network analysis to do both, investigating how strategic economic decisions, such as decisions about sharing, are embedded in and feed back onto social structure. This research is based on a questionnaire conducted with 110 Inuit households during 12 months of ethnographic fieldwork in Kangiqsujuaq, Nunavik, Canada. In Kangiqsujuaq, traditional Inuit resource harvesting and sharing practices coexist with and depend on opportunities and constraints in the cash economy. Food sharing in Kangiqsujuaq emerges as a complex social, political, and economic phenomenon that accomplishes different objectives for actors based on their social position. The network approach adopted in this research highlights the conjugate role of individual decisions and structural constraints in broader processes of social and cultural change. In the mixed economy of Kangiqsujuaq, food sharing, social structure, and political influence are intimately connected. The results suggest that economic and political inequality in the settlement are reinforced by the social structures produced through sharing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".