Exploring the Impact of Nigeria’s Oil and Gas Local Content Policy on Higher Education Institutions: A Social Network Analysis Approach
Bibliographic record
Abstract
This study employs social network analysis (SNA) to examine the impact of Nigeria's local content development (LCD) policy on higher education institutions (HEIs), specifically focusing on employment and training. Drawing from a multiplicity of stakeholders identified, these were subjected to critical scrutiny and seven key stakeholders were selected: the Federal Government, HEI lecturers, students, young employees, the Nigerian University Commission (NUC), the Nigerian Content Development and Monitoring Board (NCDMB), and industry players from both multinational and indigenous oil and service companies. Using documentary evidence and 32 semi-structured interviews, the study found a significant gap between HEL and FGN, however, there appears to be close ties with HEL and other stakeholder groups and the same is the case with FGN. Betweenness centrality values ranged from 0.0 to 1.417, with an overall network density of 76%, positioning the Federal Government as a central broker. However, a 24% residual unconnectedness suggests significant gaps in stakeholder relationships, corroborated by interview responses. These findings underscore critical implications for the development of Nigeria’s HEI sector and the broader scope of policy implementation, highlighting the necessity for enhanced stakeholder collaboration to fulfil the LCD policy’s objectives as per the NOGIC Act.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".