Globalization, Local Government and Impeachment at Grassroots Level: Nigeria as a Case Study
Bibliographic record
Abstract
Of recent, Local Government or what is known in some other countries as Mayor is – and for the first time – attracting global attention over impeachment process and its attendant theatrics. Beginning with the Mayor of Toronto who for more than one week entertained the world on Cable Network News (CNN); to the Mayor of Kampala in Uganda; Mayor of Bogota in Colombia; and the countless number of impeached Local Government Chairmen in Nigeria, the stories are the same. Reactions are also the same across the globe; first, for the Mayors themselves, second, for their supporters and, thirdly, for other extraneous factors in the impeachment process. In all of these countries, there are deep involvements of central as well as provincial governments in the impeachments of Mayors for one reason or the other leading to series of theatrics that entertain not just the local, national and the general publics in particular but global audience in general. This paper intends to use Nigeria as a case study of not just how Federal and Provincial governments as well as other godfathers interfere with affairs of Local governments and thus render their (Mayors’) autonomies useless against the wish of the Constitutions or Charters, as the case may be, that set them up.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".