Bibliographic record
Abstract
This article examines the globalisation of corporate strategic philanthropy as played out in the Niger Delta of Nigeria — a region that has been marked by a history of state and petroleum industry collusion both in social repression and environmental destruction. Social control of the Delta has rested largely on what Watts (2001) conceptualises as ‘petro violence’, the joint security imposed by the Nigerian military and oil companies to police their installations and the environment of social unrest that surrounds petroleum extraction. In examining the extractive industry's response to social dislocation, this study focuses on the adoption of a model of partnership and participatory development by Shell Nigeria. The implementation of the social stabilisation project promoted by Shell's partnership model and facilitated by international donors and state institutions (understood in corporate strategy as a ‘leveraged buy in’), exemplifies the reciprocal formation of the corporate social governance projects and development assistance in the Nigerian context. Yet this new model's attempt to achieve social consent is partially contradicted by the corporate requirement of profit maximisation served by rising prices associated with perceived and real threats to oil supplies. The oil companies’ pursuit of a social ‘licence to operate’ thus rests uneasily with an industry whose underlying logic profits from the upward movement of oil prices, dependent on instability and violence. Her dissertation project, concerning social welfare interventions associated with the oil industry in the Mexican Gulf and the Niger Delta, is based on 16 months of field research funded by the Social Sciences and Humanities Research Council of Canada, the Mario Einaudi Center for International Studies and the Latin American Studies Program at Cornell University
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".