Assessing the effect of corporate social responsibility on community development in the Niger Delta: a corporate perspective: Table 1.
Bibliographic record
Abstract
The widespread adoption of corporate social responsibility (CSR) policies by oil transnational corporations in developing countries have led to calls for a concerted effort to better capture CSR effects. Unfortunately, capturing the impacts of CSR is not as straightforward as it might seem. In fact, oil companies operating in the Niger Delta continue to face the challenge of how to determine the success or failure of their CSR initiatives either in terms of its effect on community development or its impact on corporate–community relations. To address this problem, Shell Petroleum Development Company (SPDC) in 2013 launched the Shell Community Transformation and Development Index (SCOTDI). SCOTDI represents an innovative framework that integrates and adapts a number of international principles into a composite index in a manner that is responsive to local context. The framework is used to assess and rank the performance of the different Global Memorandum of Understanding (GMoU) clusters within the host communities of SPDC. This article suggests that SCOTDI allows for a systematic assessment of the effects of CSR on community development and provides incentive for positive inter-cluster competition for community development. However, the framework also suffers from some shortcomings that can reasonably be addressed. The article considers the theoretical and practical implications for efforts to assess CSR contribution to community development in developing countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".