Youth Political Participation, Good Governance and Social Inclusion in Nigeria: Evidence from Nairaland
Bibliographic record
Abstract
As the Nigerian population continues to increase, so does the number of youth. The population of youth (18-35 years) in Nigeria is 52.2 million (i.e. about 28% of total population) and more than the entire population of Ghana, London and Benin Republic put together. In spite of the prospects that this number holds, young people in Nigeria are largely marginalized from governance, leaving them helpless to counter their continued exclusion. This is evidenced by the lower percentage of youth that hold political and leadership positions in the country. The purpose of this study was to examine the relationship between youth political participation, good governance, and social inclusion in Nigeria. Using a quantitative approach, 1,208 youth aged 18-35, selected from Nairaland, participated in the study. Data gathered was analyzed with Spearman Correlation Coefficient and the result indicates that there is significant positive relationship between youth political participation and good governance in Nigeria (r s, (1206) = .615, p < .001) and that there is significant positive association between youth political participation and social inclusion in Nigeria (r s, (1206) = .875, p < .001). It was recommended that the government should create Leadership and Democratic Institutes [LDI] across the states of the Federation and establish an Online Leadership Orientation Agency [OLOA] to utilize various social networking sites to provide free leadership courses, webinars, and orientation on the art of governance and the promotion of social inclusion among youth.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".