Investment in Agriculture and Extractive Industry: A Panacea for National Development
Bibliographic record
Abstract
Economic diversification into agriculture and extractive industry in Nigeria has been a fascinating and crucial economic issue that deserves consideration especially as the country is shifting from mono-economy (caused by oil boom) to other viable economic sectors. The global economic meltdown and depression have stimulated countries to look into other sectors of the economy in order to enhance their national development. Hence, this study tries to examine the contribution of agriculture and extractive industry to the Nigeria’s real gross domestic product (RGDP). The study makes use of time series data gathered from CBN Statistical Bulletin ranging from 1981-2017 and employs Ordinary Least Squares (OLS) method as the statistical tool with the aid of e-views version 9. The findings reveal that agriculture has a robust and noteworthy positive impact on RGDP while the solid mineral equally has a substantial positive influence on RGDP. However, crude petroleum (proxy for crude petroleum & natural gas) has a positive inconsequential effect on RGDP. This brings the study to a conclusion that investment in agriculture and solid minerals is highly imperative at the moment. Therefore, the study has suggested that economic diversification should be focused more on agriculture and solid mineral extraction. In addition, the government should try to manage the crude petroleum and natural gas exploration so as to prevent fund repatriation and transfer to other countries due to borrowed technology.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".