An Assessment of the Management of Adhoc Electoral staff and Electoral Violence in Rivers State of Nigeria 1999 – 2015
Bibliographic record
Abstract
This study employed the interview technique, focus group discussion and non-participant observation to attempt an assessment of the nexus between the pattern of management of adhoc electoral staff and election violence in Rivers State of Nigeria from 1999 – 2015 under the guidance of four hypotheses. The study was validated using the conventional means of discussion and consultation with the project supervisors and experts in election studies. The Test-retest method was used to establish the reliability of the instruments employed for the study within which percentile ratings and chi-square were employed for data analysis. The study found that the pattern of management of adhoc electoral staff contributed to the rise in the level of election violence within the state under review given that lack of transparency in recruitment, inadequate training, inadequate remuneration and absence of disciplinary measures for erring adhoc electoral staff were of primary significance in election fraud and attendant violence. The study also found that the process of recruitment of adhoc staff was rarely transparent. The study recommends that the government needs to evolve a reliable process of adhoc staff recruitment while providing for adequate training and remuneration in future elections.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".