Attentiveness to Community Needs and Corporate Social Responsibility in the Niger Delta Region
Bibliographic record
Abstract
Abstract Given the fact that most literature on the activities of the Oil Multinational Corporations (MNCs) in Niger Delta region focuses on corporate inattentiveness to the needs of the communities and the chronic corporate and government socioeconomic irresponsibility, this study used Stakeholder theory and critical ethnography research method to examine the relationship between attentiveness to the needs of communities in the Niger Delta region and Corporate Social Responsibility (CSR). While acknowledging that CSR has several definitions, we support McWilliams and Siegel (2001) 's assertion that CSR is the ability of a business corporation to go beyond legal and ethical compliance by engaging in business practices that seem to encourage some social good, above the interest of the corporation and that which is stipulated by law. The major aim of this paper was to offer practical solutions to the challenges facing natural gas development and exploitation in Nigeria. Data collected from five oil producing communities (Obagi, Obelle, Omoku, Ogbogu, and Obite) in Niger Delta between 2010 and 2013 were analyzed. The adoption of critical ethnography research method allowed these authors to objectively present their findings. This study concludes that only management practices, which support open and continuous dialogue with all the stakeholders and corporate socioeconomic policies that favour socio-cultural, environmental and economic concerns of all stakeholders will reap the full benefits of the Nigerian emerging economy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| 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".