Economic System of Islām as a Panacea to Economic Recession in Nigeria
Bibliographic record
Abstract
The National Bureau of statistics in July 2016 officially announced that Nigeria has entered into a recession, its first in 29 years. According to the data provided by the World Bank, the most recent time that the nation saw such a significant instance of economic downturn was while Ibrahim Babangida was in power. During that time period, the economy of Nigeria had a decline of 0.51% in the first quarter of 1987 and 0.82% in the second quarter of the same year. The disaggregation of the results for 2016 into its individual quarters revealed the sharpest decline, which was 2.4 percent, and a turning point after that. This is going by the manifestation of economic hardships experienced by the populace, even up to this moment. In point of fact, Nigeria's economy went from a level of economic buoyancy to a level of economic recession in the second quarter of 2016, and it has maintained negative growths in all quarters of that year up to 2017. This downward trend is expected to continue throughout 2017. In a bid to cushion the effect of this situation, the government in the short and medium terms took some measures (policies and reforms) to reverse the recession and put it on the path of growth reliance. In spite of these encouraging developments and despite the fact that it has been two years since the National Bureau of Statistics (NBS) officially declared that Nigeria had emerged from recession in its quarterly report on the country's Gross Domestic Product (GDP), the country's economic situation is still difficult, particularly with regard to the continued high cost of food. For a long-term solution, this paper, therefore, examines some measures of Islamic economic system, which is recession proof, due to the fact that it is basically premised on the fundamental sources of Islam, Sharī῾ah. The paper concludes that if these measures are taken into consideration by the government, it will not only lift the economy out of its current phase but will also guarantee a secure economy. The paper adopts analytical research methods, using majorly secondary data collection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".