Indigenous food production in a carbon economy
Bibliographic record
Abstract
Indigenous communities in the North American Arctic are characterized by mixed economies that feature hunting, fishing, gathering, and trapping activities—and associated sharing practices—alongside the formal wage economy. The region is also undergoing rapid social, economic, and climate changes, including, in Canada, carbon taxation, which is impacting the cost of fuel used in local food harvesting. Because of the importance of local foods to nutrition, health, and well-being in Arctic Indigenous communities, there is an urgent need to better understand the sensitivity of Arctic food systems to social, economic, and climate changes and to develop plans for mitigating potential adverse effects. Here, we develop a Bayesian model to calculate the substitution value and carbon emissions of market replacements for local food harvests in the Inuvialuit Settlement Region, Canada. Our estimates suggest that under plausible scenarios, replacing locally harvested foods with imported market substitutes would cost over 3.1 million Canadian dollars per year and emit over 1,000 tons of CO 2 -equivalent emissions per year, regardless of the shipping scenario. In contrast, we estimate that gasoline inputs to harvesting cost approximately $295,000 and result in 315 to 497 tons of emissions. These results indicate that climate change policies that fail to account for local food production may undermine emissions targets and adversely impact food security and health in Arctic Indigenous communities, who already experience a high cost of living and high rates of food insecurity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".