Analysis of the Impact of Non- Oil Sector on Economic Growth
Bibliographic record
Abstract
In the period of the 1970s, Agriculture was the main stay of the Nigerian economy. The oil boom of 1970s brought about a gradual shift from agriculture to crude oil making Nigeria to depend heavily on petroleum as a main source of foreign exchange earnings. Agricultural sector which use to be the back bone of the economy was rendered competitive over time. The crux of this of this paper is to analyses the impact of non-oil export on the growth of the Nigerian economy. Data were obtained from secondary source mainly from Central Bank of Nigeria Statistical Bulletin, annual reports and statements of account. The ordinary least square (OLS) statistical tool was used to analyze the data. The findings revealed that non-oil export has positive effect on the growth of Nigerian economy during the period under review, though the performances in terms of output level and revenue generation was below expectation. The paper recommended the need to increase production in both agricultural and manufacturing sectors to ensure product availability for both local and export purposes. Also, there is need to complete the export processing zones in earnest to promote the establishment of export oriented firms that will produce solely for export market. Key words : Exchange rate; Foreign exchange; Oil boom; Revenue
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".