Shell Nigeria’s Global Memorandum of Understanding and corporate-community accountability relations
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine whether Shell Nigeria’s Global Memorandum of Understanding (GMoU) promotes corporate-community accountability as a basis for fostering sustainable community development in the Niger Delta. Design/methodology/approach Shell Nigeria’s GMoU stand-alone reports were analysed through the lenses of accountability and transparency theoretical frameworks to explore the extent to which GMoU, as a corporate social responsibility (CSR) initiative, is dialogically embedded and practised. Meaning-oriented content analysis was deductively used to isolate pertinent themes and generate findings from the background theoretical literature. Findings The authors find that Shell discursively appropriates the meaning of accountability and transparency in a manner that allows it to maintain its social legitimacy and the asymmetric power relations between itself and host communities whilst restricting communities’ agency to hold it accountable. Shell does this by interpreting the notion of participation restrictively, selectively deploying the concept of transparency and accountability and subtly exerting excessive control over the GMoU. Thus, the GMoU’s potential to contribute to sustainable community development and positive corporate-community relation is unlikely tenable. Originality/value Accountability and transparency are core and critical to corporate-community relations and for achieving community development CSR objectives, but are often taken for granted or ignored in the CSR literature on the Niger Delta of Nigeria. This paper addresses this gap in the literature by using accountability and transparency lenses to unpick GMoU model and contribute to studies on CSR practices by oil multinational corporations (MNCs) in developing countries. Indeed, the use of these lenses to explore CSR process offers new insights as to why CSR practices have failed to contribute to sustainable community development despite increased community spending by oil MNCs.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".