Vote Buying: Examining the Manifestations, Motivations, and Effects of an Emerging Dimension of Election Rigging in Nigeria (2015-2019)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Elections provide the platform for the electorate to choose their leaders in modern democracies. In Nigeria, they provide the opportunity for rich corrupt politicians to perpetrate acts of vote buying against both fellow contestants and the electorate. The introduction of Smart Card Readers (SCRs) technology and the permanent voter cards (PVCs) by the Independent National Electoral Commission (INEC) made it difficult for politicians to manipulate election results. In other to game the system, politicians began relying increasingly on vote buying as a means of compromising and influencing the outcome of elections. Hence, vote buying is a fairly new method of election rigging. This paper, therefore, intends to explore the manifestations, motivations, and effects of vote buying on elections conducted between 2015 and 2019, as well as its implications for future elections in the country.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it