Mechanisms Used by Multinational Oil Companies to Derail Human Rights and Environmental Litigations Arising from the Niger Delta
Bibliographic record
Abstract
Abstract Multinational oil companies (MNOCs) usually claim that they have several obligations to protect human rights and the environment where they operate and to resolve any disputes with local communities arising from their operations in the shortest possible time. However, the combative approach taken by MNOCs (e.g. several interlocutory appeals, challenging the legal standing of plaintiffs) during human rights and environmental litigations undermines these obligations because it continually denies, delays, and derails justice for the local communities. The aim of this paper is to discuss the mechanisms used by MNOCs to derail human rights and environmental litigations arising from the Niger Delta. This paper uses a comparative legal approach combined with a cross-case analysis of a selection of transnational litigations to highlight several mechanisms that fall into eight (8) categories related to oil operations – transparency, disclosure, bribery and corruption, labour/employee rights, safety and security, delays in litigations, pollution, remediation and compensation. The paper concludes that mechanisms used by MNOCs (e.g., Shell), as indicated in recent ligations arising from the Niger Delta, are at odds with their human rights obligations, thus affecting effective remedies for the people whose human rights have allegedly been affected by corporate conduct.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".