“Measurement drives diagnosis and response”: Gaps in transferring food security assessment to the urban scale
Bibliographic record
Abstract
The understanding of food security has seen major shifts since the original conceptualisations of the challenge. These changes in understanding have been accompanied by different food security measurement approaches. Despite the fact that the world has become increasingly urbanised and the developing world in particular, is experiencing its own urban transition, changes in food security measurement remain predominantly informed by a rural understanding of food security. In instances where urban measurement does take place, rural-oriented measurement approaches are adopted, occluding critical urban challenges and systemic drivers. This paper begins by highlighting the urban transition and attendant food security challenges in the Global South. It then reflects on existing food security measurement methods, detailing the positive components but also highlighting the shortfalls applicable to the urban context. At the urban scale, a food system assessment is argued to be one appropriate tool to respond to urban food insecurity while at the same time providing both the “breadth and depth” to inform effective food security programming and policy interventions. Theoretically, questions of scale, context and a critique of the rural bias in food systems work are essential informants guiding the approaches applied.
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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 | 0.179 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.011 | 0.029 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".