Assessing supermarket patronage in Matola, Mozambique
Bibliographic record
Abstract
Abstract As an indicator of a potential broader nutrition transition, the supermarketization of urban food systems in the Global South has become a growing area of research interest. While the rising dominance of supermarkets in urban food systems has been noted in several global cities in the Global South, there have been fewer investigations into the spatial and demographic characteristics that may govern the patronage of supermarkets in smaller secondary cities. This paper assesses this supermarketization trend via an investigation of supermarket patronage in a secondary city through a 2014 household survey of Matola, Mozambique (n = 507). Using a combination of descriptive statistics and decision tree learning algorithms, the findings suggest a strong geographic pattern to supermarket patronage among the surveyed households in Matola. Further analyses comparing frequent and infrequent supermarket patrons confirms the observation that spatial distance may be a more significant determinant of supermarket patronage than household wealth among the surveyed households in Matola. These findings suggest that the spatial availability of supermarkets may play a greater role in defining the supermarketization of Matola’s food system than household entitlements. These findings also have implications for the evolving concept of urban food deserts in secondary cities, recognizing the role of spatial location in determining household access to supermarkets.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".