Amazon in the Arctic: E-Commerce, Infrastructure, and Alimentary Assemblages in Nunavut, Canada
Bibliographic record
Abstract
Since establishing a delivery hub in Iqaluit, Nunavut in 2020, Amazon.com, Inc. has become an essential resource for many Nunavut residents, providing affordable access to goods otherwise constrained by high costs and limited availability in the Arctic. This article explores the significant yet underexamined role of the Amazon corporation in Nunavut, Canada, as a response to the territory's infrastructural and economic challenges. By combining an alimentary understanding of infrastructure with the theoretical concept of assemblage theory, I analyze Amazon's operations as part of a complex system shaped by global logistics and local agency. My research based on ethnographic fieldwork highlights how Inuit residents adapt Amazon's services to navigate gaps left by government programs. This nuanced perspective challenges assumptions about e-commerce as a purely homogenizing force, illustrating how residents adapt global platforms to meet local needs, and underscoring the interplay between global corporations, local infrastructure, and Indigenous agency in shaping Arctic livelihoods.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".