Bibliographic record
Abstract
Democracy is based on the principle of the majority able to choose who leads them in a free and fair context devoid of external interference and political influence. The right to elect a wrong candidate is even part of democracy. The law cannot regulate the legitimate choices that the democratic free will is entitled to make. It chooses what it will. It rejects what it will not choose, or else the democratic free will ceases to be what it fundamentally ought to be, namely “free”. Vote trading is a concept in the Nigerian democratic experience. The issue of vote-trading has been in Nigeria's democracy since its inception but became prominent during the present democratic dispensation. Vote buying has been serving as a clog in the wheel of free choice which is the hallmark of a democracy. Unfortunately, not all people that being influenced by vote-buying know what is going on. Some people indulge in the act of vote-trading unknowing. This study which is mainly based on literature and conceptually looked at the influence of vote trading on voter’s free choice, the factors that influenced both vote buying and selling, and how it can be curbed. Consequently, past literature, like journals, books, and other publications on vote-trading were considered in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".