Corporate Social Responsibility and Insecurity in the Host Communities of the Niger Delta, Nigeria
Bibliographic record
Abstract
This study investigates the corporate social responsibility (CSR) strategy by multinational corporations (MNCs) in the Nigerian oil and gas industry. The goal of CSR is to encourage a positive impact through its activities with the stakeholders, the environment and the general public. CSR also focuses on how businesses would proactively support the public interest by encouraging community growth and development. The problem of insecurity in the Niger Delta region is attributed to the feeling of anger and frustration by host communities due to perceived negligence of CSR initiatives by the MNCs. This has resulted in crude oil theft, vandalization of oil pipelines, general insecurity and actions that have negatively affected the activities of the MNCs as well as the federal government who depend on the oil revenue for its national budgets. This paper considers the CSR initiatives of the MNCs and the underpinnings of security challenges in this region. This is an empirical paper based on in-depth semi-structured interviews conducted in the host communities of the Niger Delta region. Using the stakeholder theory, the paper maintains that initiating and implementing the right CSR strategy would help to reduce the crisis in this region and enhance the peaceful operations of the MNCs. It contributes to emerging discourse in CSR on how desired positive impact can be made through effective CSR.
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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 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".