Managing University Congregation Election in Nigeria for Better Result
Bibliographic record
Abstract
The study investigated the conduct of university’s congregation election following the general complaints by university staff of poor organization and conduct. The population consisted of all principal officers and all graduates employed by the various universities in the three geo-political zones of Nigerian federation: North-Central, Southeast, and Southwest. From these zones, a sample of two federal and two state universities were selected by stratified random sampling method for the study. A stratified random sampling method was used to select 200 (100 academic and 100 non-academic) staff from each of federal government universities while 100 (50 academic and 50 non-academic) staff was selected from state universities. A total sample of 900 academic staff (450 males and 450 females) and 900 administrative staff (450males and 450 females) participated in the study. The data of the study were collected using questionnaire. The questionnaire was titled Management of Congregation Election Inventory (MCEI). It was made up of two parts – Part A was demographic while Part B contained 12 questions bothering on organization of previous elections, management and outcome of previous election results. The validity of the questionnaire was made by the experts in educational administration and planning and also in political Science. The reliability of the instrument was tested using a Split-Half Method. The Correlation Coefficient was corrected by the use of Spearman Brown Formula. The Pearson Product Moment Correlation was .75 and final Spearman Brown Formula yielded 0.82. The major finding was that election was poorly organized and conducted, Based on the findings, the major recommendation was a surgical change in the organization and conduct of congregation election.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".