Local Content Policy, Human Capital Development and Sustainable Business Performance in the Nigerian Oil and Gas Industry
Bibliographic record
Abstract
This study examined the extent to which the Local Content Policy has impacted on human capital development and sustainable business performance in the Nigerian Oil and GasIndustry, following the enactment of enabling legislation. Primary data were employed, which were obtained through the administration of structured questionnaire to purposively selected oil servicing companies in Niger Delta, the home to more than eighty percent of the indigenous oil companies in Nigeria. The results showed that Local Content Policy had significant impact on the development of human capital in the Oil and Gas Industry. There was a paradigm shift in the educational capacity of the Management of the oil servicing firms as over 70% of them had at least first degree or its equivalent. Through oil sector linkages, the firms had strengthened their absorptive capacities to internalize the technological and managerial skills that flow to them. This had consequently boosted the business performance of indigenous oil servicing firmsin terms of growth in profit, market share and returned on investment (ROI). The study concluded that the Local Content Policy had achieved significant success in enhancing the development of human capital which in turn positively influenced business performance of indigenous companies in the Oil and Gas Industry in Nigeria.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".