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 ( MNOC s) 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 MNOC s (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 MNOC s 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 MNOC s (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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".