Bibliographic record
Abstract
Tribalism is coined from ‘tribes’, an alternative word for ethnic or linguistic groups or in some countries ‘nation’ or ‘nationality’. Tribes supply a lot of Nigeria’s diversity providing traditional costumes, dress, music, dancing, indigenous language, arts, folklore, religion, all of which can constitute an asset to a people. It is naturally regarded as a small group, a human social organization defined by ‘traditions of common descent’ having temporary or permanent political integration above the family level with a shared language, culture or ideology. Encyclopedia Britannica asserts that tribe members ‘share a tribe name in a contiguous territory, and engage in joint endeavours such as trade, agriculture, house construction, warfare, economic and business activities and warfare. They often stay in small cluster-communities which can grow into large communities and even a nation. This paper attempts to critically examine the multiple play-outs of Nigeria’s many tribes and nationalities during and after colonialism, the intricate connection between tribalism and politics, leadership and the evolution of the Nigerian polity, the grievous harm as well as advantages of tribalism to Nigeria’s evolution. The tribe is always a major factor in the country and in its people. It ends with specific prognosis and a few recommendations.
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.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".