Economic Diversification through Solid Minerals Sector in Nigeria: Potentials, Challenges and Prospect
Bibliographic record
Abstract
This study examined the potentials, challenges and prospect of solid minerals sector of the Nigerian economy; with emphasis to factors responsible for the low performance of the sector in terms of contribution to GDP, export and employment generation. This sector has prospect to promote economic diversification drive of the Federal Government of Nigeria if well developed and managed. Despite government efforts over the years to reposition and revitalize it through legislation, regulation and investment; its optimum output in terms of wealth creation, government revenue, employment generation have been hampered by a number of factors that did not allow the sector to prosper as planned. The study employed Documentary Research Method (DRM) as a source of data; and document analysis in interpreting and presenting research findings. From 1981 – 2022, solid mineral had a positive but statistically insignificant impact on the Nigerian economic growth including export. Nigeria’s Mining and Quarrying sector contributed 7.72% to the overall GDP in the third quarter of 2024, while it recorded 8.32% in the third quarter of 2023 according to the NBS report, 2024. Some of the challenges of the sector include access to high capital and technology; insecurity; poor private sector investment, energy intensive, local human capital, artisanal mining, illegal mining in many solid minerals’ sites, value addition, developing and building investors’ confidence as well as regulatory framework that can guarantee fair fiscal regime among others. In view of this, there is the need for the Federal Government of Nigeria through Nigerian Geological Survey Agency (NGSA) to provide data about locations of solid minerals in commercial quantities in the country for attracting local and foreign investment. Next is to ensure political stability and security in the country; promote forward and backward linkages in the solid minerals sector; provide basic infrastructure and address illegal mining activities across the mining sites in the country.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".