Influence of Vote Buying Among Electorates; Its Implications to Nigeria Future Democracy
Bibliographic record
Abstract
The 2019 general elections in Nigeria witnessed an explosion in the use of the term “vote buying” in academic and media circles. An often-cited definition of vote buying describes it as “the exchange of private material benefits for political support”. Vote buying is seen as a contract, or perhaps an action in which the voter sells his or her vote to the highest bidder. The issue of vote buying has become a problem in the nation polity and the fear is can the university students who serve as adhoc staff of INEC be exonerated from this menace? The researcher adopted a descriptive survey design for this study. Purposive random sampling technique was adopted in the selection of the sample from four Universities in South West. One hundred (100) participants were randomly selected from each universities.The instrument for this study was a self - constructed questionnaire. The questionnaire was divided into two sections A and B. Section A sought personal information of the participants. Section B consisted of 15 items. The finding from the work shows that the respondents among others aware of the danger the vote buying could pose to our future democracy. It is therefore recommended that political education be included in all level of education and government should encourage all organizations including religion organization to always enlightening their members the negative effects of vote in buying.
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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 | 0.006 |
| 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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".