Bibliographic record
Abstract
Various indicators suggest that poverty is a major obstacle to Nigeria’s socio-economic development. Poverty has persisted and several interventions have failed to yield significant improvement in Nigeria’s Human Development Index even in periods of economic growth. Plagued with the challenges of unemployment crises, climate change, conflict, fragility and violence, Nigeria (the most populous country in Africa) stands at a grave risk if poverty is not tackled. This paper explores seven theories of poverty in literature: The Culture of Poverty, Individual Deficiency Theory, Progressive Social Theory, Geographical Disparities Theory, Cyclical Interdependence Theory, Poverty Individualisation and the Theory of Social Exclusion /Cumulative Disadvantage. It reviews these theories by employing a qualitative and descriptive research approach in order to broaden the understanding of the complexities of the phenomenon of poverty from a global worldview and examine how these relate to the nature of poverty in Nigeria. It corroborates the fact that poverty in Nigeria is complex and multidimensional in its conceptualization and measurement, encompassing economic, social, cultural and psychological indicators. The paper therefore attempts to explore the phenomenon of poverty within the Nigerian context by examining these theoretical paradigms. It suggests an understanding of underlying causal factors of poverty in designing pro-poor programmes and a hydra-headed approach to tackle its menace effectively and progressively. It argues that poverty reduction is realizable by empowering people to develop resilience to cope and overcome it within the scope of their resources and capabilities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| 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".