Youth Political Participation, Good Governance and Social Inclusion in Nigeria: Evidence from Nairaland
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it