Governing Nigeria 100 Years after Amalgamation: From a 'Mistake' to Miracle?
Bibliographic record
Abstract
Given the ethno religious diversity of the various groups that presently constitute modern Nigeria, the amalgamation of southern and Northern Nigeria protectorates by British colonizers in 1914 has been widely referred to by many observers and scholars as a mistake in colonial administration. As such, even as the centenary celebrations of the countrys amalgamation have begun, critics staunchly argue for a more sober reflection on the countrys progress so far. After 100 years, Nigerias dismal socioeconomic and political development constitutes a paradox of plenty understood as high poverty amidst enormous human and material resources. At the heart of the countrys unenviable rank as one of the poorest countries in the world lies a governance crisis characterized by institutionalized systemic corruption and socioeconomic cum political mismanagement. This paper thus examines the crisis of governance in Nigeria since amalgamation to the current democratic dispensation. The paper combines historical analysis with contemporary political-economic explanations to showcase the problems of institutionalized corruption and misgovernance as primary causes of underdevelopment in Nigeria. The prospects for a more prosperous country after 100 years of amalgamation are then examined and important recommendations are stated as means of rebuilding the Nigerian state as a miracle.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".