Introduction: African Secondary City Food Systems in Context
Bibliographic record
Abstract
Abstract Countries across Africa are rapidly transitioning from rural to urban societies. The UN projects that 60% of people living in Africa will be in urban areas by 2050, with the urban population on the continent tripling over the next 50 years. The challenge of building inclusive and sustainable cities in the context of rapid urbanization is arguably the critical development issue of the twenty-first century and creating food secure cities is key to promoting health, prosperity, equity, and ecological sustainability. The expansion of Africa’s urban population is taking place largely in secondary cities. These are broadly defined as cities with fewer than half a million people that are not national political or economic centres. The implications of secondary urbanization have recently been described by the Cities Alliance as “a real knowledge gap,” requiring much additional research not least because it poses new intellectual challenges for academic researchers and governance challenges for policymakers. International researchers coming from multiple points of view, including food studies, urban studies, and sustainability studies, are starting to heed the call for further research into the implications for food security of rapidly growing secondary cities in Africa.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".