Exploring the Role of the Mixed Methods Approach in Facilitating an Improved Understanding of Food Access in Masiphumelele
Bibliographic record
Abstract
This thesis focuses on the food environment of Masiphumelele and seeks to understand how mixed-method approaches could be employed to facilitate an advanced understanding of food access in Masiphumelele. Masiphumelele was chosen as the case study for this research project as this work is sponsored by the IDRC-funded Nourishing Spaces project, which works in Masiphumelele. Based on the food-environment framework, this thesis embarked on the valuation of food environment literature to establish the interconnections between the four pillars of food security, food environments, and food systems in the urban context. The findings of this study endorse other work on food environments that issues of access should not only be understood through the physical and socioeconomic lens but also consider related social aspects that shape, enable or constrain food choices and behaviours in urban contexts. The findings of this thesis underscore the need for the reconceptualization of food environments beyond the simplistic physical and economic access framings which dominate earlier food environment work characterized by food deserts. Following the assessment of existing literature on food security, the discourse revealed that adopting the mixed-method approach that integrates participatory and retail mapping is an applicable conceptual framework for exposing socio-spatial dynamics influencing food utilization and food accessibility in the urban context. Building on the growing scholarly and policy interest of mixed methods approaches this thesis endeavours to establish the significance of the mixed-method approach in facilitating an improved understanding of food access in Masiphumelele.
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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 | 0.073 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".