Ethnic identity as a social cleavage in Nigeria
Bibliographic record
Abstract
Adopting Lipset and Rokkan’s submissions which suggest, social cleavages as resulting from conflict groups based on perceptions of association in opposition to other such groupings among large segments of a population, the study argued that ethnicity is the single most important embodiment of social cleavages in Nigeria. The study relies on secondary methods of data collection; further stressed that in the absence of formidable class structures, ethnicity plays a crucial role in defining individual identity in relation to groups, derivative of norms, and intermediaries between the society and the state. Building on the pedestals of the ethnic competition model, we further argued that central to the mobilization of ethnicity is the presence of opposing groups and ethnic elites. Beyond the potency for conflictual group relations, the ethnic competition model was adopted to account for the widespread predisposition to compete along ethnic lines in socially diverse societies like Nigeria. The study concludes by stating the very significance of ethnicity as a social capital in Nigeria, derives from its social acceptance and mobilizing properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".