Effect of business on economic development in Nigeria
Bibliographic record
Abstract
The study examines the effect of the businesses in Nigeria on the economic development of the country. This is with the desire to examine its ability to achieve the desired objectives in the country (Nigeria). Business has contributed to changes in the level of development in the country through generation of employment; direct creation of wealth and reduction of poverty by contributing to the Nigeria Gross National Product (GNP) and GDP. Also, it contributes to provision of: technical innovation and competition for better services and meeting needs of other businesses by providing products or raw materials needed for other businesses to survive. The increase in economic output recorded in third quarter of 2013 was as a result of increases recorded in agriculture, hotels and restaurants, building and construction and telecommunications sectors of the economy. The contribution of the non-oil sector in the third quarter of 2013 was due to benign weather conditions that led to bountiful harvests in the agricultural sector, increased investments by local and foreign investors and the positive macroeconomic environment. The report however identified the privatization of the power sector, agricultural transformation initiative among factors to drive the country’s growth. The study made recommendation for further development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".