Living with insecurity: Food security, resilience, and the World Food Programme (WFP)
Bibliographic record
Abstract
As the world’s largest humanitarian organization fighting hunger, and primary expositor of food insecurity in sub-Saharan Africa, the World Food Programme’s (WFP) activities offer a unique opportunity to examine the contemporary food-security nexus. In this article, we examine the ‘turn’ toward resilience in the practices and policies of the WFP. Our analysis emphasizes that resilience is one of a family of security strategies through which the WFP seeks to govern food security. As such, it is impossible to claim, as some have, that resilience is displacing security as the dominant logic for governing insecurity. Nevertheless, resilience is a cornerstone of the WFPs’ current activities. Whereas more familiar strategies of security attempt to pre-empt or contain disruptive events – in the context of food crises – resilience is a style of thinking that assumes the inevitability of unpredictable, high-impact events and aims to foster the capability for systems and people to adapt, absorb, and bounce back from their effects. Resilience, while championed as part of an overall solution to a range of ills afflicting human populations today, aims only to equip people and populations with the capacity to live with the instabilities of a neoliberal food system without questioning, destabilizing, or resisting the very sources of socio-economic and political instability.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".