The Impact of Political Leadership and Corruption on Nigeria’s Development since Independence
Bibliographic record
Abstract
The paper draws an interlocking relationship between political leadership and development and concludes that, while leadership had played tremendous role in the socio-political and economic development of most nations of the world, the reverse is the case in Nigeria. Apart from identifying other social vices that accounted for the protracted state of Nigeria’s underdevelopment, the paper also singles out corruption as the major impediment to Nigeria’s quest for development since independence. Drawing from the World Bank, Transparency International and highly knowledgeable scholars in this field, the paper demonstrates the process through which Nigerian political leadership became ‘neck-deep’ in corruption with several cases of monumeotal diversion of public funds meant for the economic development of the country into individual pockets. The multi-dimensional consequences of corrupt practices on a nation’s socio-political and economic development cannot be overemphasised, as virtually all sectors of the country, including education, health, agriculture, politics, technology, e.t.c, are negatively affected, with the resultant outcome like extreme poverty, high level of illiteracy, economic dependency, technological backwardness, political instability, e.t.c, as the order of the day. Nigeria’s situation typifies the above as shown in the paper.
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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 | 0.001 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".