Do Dietary Changes Increase the Propensity of Food Riots? An Exploratory Study of Changing Consumption Patterns and the Inclination to Engage in Food-Related Protests
Bibliographic record
Abstract
Following widespread food riots in 2008, many people argued that high food prices cause political instability and civil unrest in the form of food riots. However, subsequent research has demonstrated that political, cultural, and economic factors confound the impact of price in determining whether a food riot occurs. This paper contributes to this growing body of literature by exploring: (1) the relationship between household demographic characteristics and reported intent to riot due to future food price rises; and (2) the relationships between people’s diets and their reported intent to riot due to future food price rises. We hypothesize that local context, including demographic factors and dietary patterns, combine to predispose some groups of people to riot when food prices rise. This hypothesis is tested using household surveys (N = 300) and three focus groups discussions (N = 65) carried out in three cities in the Central African nation of Cameroon that experienced widespread food riots in 2008. Results show that some 70% of the respondents would riot if food prices went up. Also, in the event of food price rises: (1) households in Cameroon’s major cities are more likely to riot than the citizens of smaller cities; (2) Households with relatively higher educational level, high incomes, are less likely to riot. Finally, the relationship between dietary patterns and propensity to riot is not straightforward as changes in consumption of different food groups influence propensity to riot in different ways. Overall, this paper demonstrates that preemptive strategies designed to avoid future food riots in Cameroon must take into consideration these spatial, demographic, and dietary factors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".