‘Good Policy’ Gone Bad: Institutionalised Ranking of Citizens and Identity Conflicts in Nigeria
Bibliographic record
Abstract
Contrary to mainstream thinking, the existence of diversity does not necessarily condemn a state to instability and chaos. In fact, difference is a good ingredient for progress because each group contributes its unique experiences and peculiar qualities to the cause of a nation. To produce crisis, diversity must be worked on or manipulated. With a view to gaining deeper understanding of the factors that impact on identity diversity to create conflict in Nigeria, a number of conflict theatres in the country were examined. Having explored each case, the article notes that the policies of the colonial state, that emphasised group differentiation, laid the foundation for identity conflicts. The post-colonial state is just as guilty. Its administrative arrangement for managing diversity, the federal character principle (FCP), has failed to exploit the country’s diversity to produce development. Instead, it has deepened the isolation of certain groups, thus, inhibiting national integration. Added to the deliberate manipulation of diversity for personal interests by the political class, the incidence of identity conflicts in the country can be explained. Going forward, the author stresses the need to review the FCP and close all loopholes in its enabling laws that allow for easy misinterpretation and deliberate misapplication.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".