Investigating perishable food supply chain management challenges in remote Arctic communities: insights from an embedded case study
Bibliographic record
Abstract
Purpose Food insecurity in remote Arctic communities is a significant public health issue, driven in part by limited access to affordable, high-quality fruits and vegetables (F&V). This paper examines how northern perishable food supply chain (NPFSC) management challenges affect the affordability, quality and availability of F&V in these communities. Design/methodology/approach A qualitative, embedded single-case study was conducted in a remote city in Nunavut, Canada, triangulating semi-structured interviews, a focus group and field observations. Findings Procurement dependencies, logistical constraints and gaps in quality and information management are key drivers of inefficiencies in NPFSC management. High asset specificity, environmental uncertainty and information asymmetry generate elevated ex-post transaction costs that shape supplier, carrier and retailer behavior, ultimately leading to higher prices, limited availability and compromised quality of fresh produce. Weak contracts, unclear property rights and fragmented information flows diffuse accountability and foster opportunism, while existing subsidy programs fail to address these critical governance and coordination gaps. Building on these insights, the study develops theory-driven propositions emphasizing relational governance, collaborative and technology-enabled logistics, clearly defined property rights through structured contracts, and the integration of enforceable quality standards into subsidy programs to improve food access in remote regions. Originality/value By integrating transaction cost economics with supply chain management and perishable food research, this study deepens understanding of how NPFSC dynamics shape food affordability, availability and quality and offers actionable insights for building more resilient and accountable supply chains in remote regions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 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".