Cash Economy and Store-Bought Food Biases in Food Security Assessments of Inuit Nunangat
Bibliographic record
Abstract
Researchers, community organisations, and Inuit leaders increasingly question the suitability of methods to assess the prevalence of food insecurity in Inuit Nunangat (the Inuit homeland in Canada). Of particular contention is the United States Department of Agriculture’s (USDA) Household Food Security Survey Module (HFSSM), applied in modified form as part of Health Canada’s nationwide Canadian Community Health (CCHS) and Aboriginal Peoples Surveys. The 18-question HFSSM is the primary survey tool used by the Government of Canada to assess food security prevalence, yet the Module asks only about the affordability of store-bought foods (also termed ‘market foods’ elsewhere in literature) when collecting data to designate food security status. This is despite communities in Inuit Nunangat having complex ‘dual’ or ‘mixed’ food systems and foodways: relying on foods harvested from ancestral lands (country foods) in combination with store-bought foods to sustain mixed cash-subsistence economies and diets. Sourcing country foods requires money for the purchase of equipment and machinery. However, they also have numerous access and availability criteria dictated by non-financial factors. In this paper, we explore the problem of the monetary bias (the focus on an individual or household’s ability to purchase foods) in the HFSSM and discuss the knock-on effects of using monetary metrics as the sole means of measuring and monitoring food security in dual food environments. We contend that relying on monetary access as a measure presents an incomplete picture of the reality of food insecurity in Inuit Nunangat. Presently, there is little consideration of the nuance of social norms and cultural values that govern dual food systems or the importance of less tangible non-financial factors that might affect food access (e.g. knowledge of where and how to harvest and maintain machinery, suitable environmental conditions for travel, conducive harvest regulations, social relationships, and ecological stability). Ultimately, this contributes to restricted policy-level understandings of what it means to ensure stable, culturally adequate, and just food systems, and limits self-determination in northern food environments.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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 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".