Eat Widely, Vote Wisely? Lessons from a Campaign Against Vote Buying in Uganda
Bibliographic record
Abstract
We study a large-scale intervention designed by civil society organizations to reduce vote buying in Uganda’s 2016 elections. We study this intervention in light of a model where incumbents benefit from a first-mover and campaigning advantage, vote buying and on-the-ground canvassing are complementary, and voter reciprocity increases the effectiveness of vote buying. The interventionundermined reciprocity as well as the drivers of the campaigning advantage of incumbents. As a result, challengers not only canvassed more intensively but also bought more votes in treated locations. Consistent with incumbents being first movers in markets for votes and facing more frictions to adjust their tactics than challengers, their response to the intervention was limited. The intervention ultimately failed to reduce vote buying, but led to short-run electoral gains for challengers and increased service delivery in treated locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".