The Work of Hunger: Security, Development and Food-for-Work in Post-crisis Jakarta
Bibliographic record
Abstract
Food-for-work programs distribute food aid to recipients in exchange for labor, and are an important mode of aid delivery for both public and private aid providers. While debate continues as to whether food-for-work programs are socially just and economically sensible, governments, international institutions, and NGOs continue to tout them as a flexible and cost-effective way to deliver targeted aid and promote community development. This paper critiques the underlying logic of food-for-work, focusing on how this approach to food aid and food security promote labor force participation by leveraging hunger against poverty, and how the ideological and practical assumptions of food-for-work become enmeshed within discourses of geopolitical security. I rely on a case study examination of US-funded food-for-work programs implemented in Jakarta, Indonesia following the 1997 financial crisis. The crisis produced acute food insecurity and poverty in Indonesia, provoking fears of mob violence by the hungry poor and the spread of radical Islamism in the post-crisis political vacuum. Food-for-work programs were, in this context, meant to resolve the problems of both food insecurity and geopolitical insecurity by providing food to targeted populations, employment to those otherwise thrown out of work, and resituating the hungry poor in relation to broader scales of local, national, and global power.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.009 |
| 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".