Intensity of food deprivation: The integrative impacts of the world system, modernization, conflict, militarization and the environment
Bibliographic record
Abstract
US and world military expenditures have increased dramatically in the last decade. Some cross-national treatments identify positive impacts of military spending on a range of domestic outcomes, while many others point to the converse. We review the literature and then focus on under examined relationships, including the impact of military expenditures on the intensity of food deprivation worldwide. We employ a structural equation modeling technique that permits synthetic analyses of direct and indirect impacts of a range of factors specified by the theories. We find world-system context indirectly matters a great deal to the intensity of food deprivation in nations, both in our sample of developed and developing nations, and of developing countries only. So do intra-national and international conflicts, especially insofar as they impact national modernization and military spending. While modernization is moderately enhanced by military spending for our cross-national sample of developed and developing countries, it is not for the sample of developing countries only. This may point to military technology’s spill over effects on other sectors of the economy, but solely for developed nations. For the world over, national modernization, itself a consequence of global power and dependency, directly reduces the intensity of food deprivation, while military expenditures directly heighten it. These differential relationships lead us to advocate for a more synthetic theorizing in studies of food security and hunger, while accounting for global circumstances that produce both similar and different consequences in richer and poorer countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".