COVID-19 Mutual Disabilities in Edo State Gubernatorial Electoral Process
Bibliographic record
Abstract
The conduct of elections in Nigeria is generally associated with manipulation, which has often undermined the credibility and fairness of the process since the country gained its independence. COVID-19 poses a very serious challenge to the electoral process, considering the nature of the disease, which has led to the promotion of limited physical interaction as an approach to mitigate its transmission and safeguard the health of the people while at the same time impacting negatively on state's electoral justice. While elections remain a key factor to the attainment of political positions in a democratic setting globally, several scholars and media reports have made attempts to assess the political intrigues in the state of Edo as a result of the tense atmosphere created by political gladiators. The use of the pandemic and various forms of propaganda to destabilise the camp of perceived opponents with the aim of winning public support are notable strategies employed by the main contending political parties and their candidates as the election approaches. Therefore, this article evaluates the impediments and political manoeuvrings in the electoral process in the state of Edo, considering the increasing number of corona-virus infections, the country's frail electoral system, and the desire to maintain credible democratic consolidation in the country.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".