Corporate Social Responsibility and Development in Africa: Issues and Possibilities
Bibliographic record
Abstract
Abstract The literature on the relationship between Corporate Social Responsibility (CSR) and development in Africa is only just emerging, and it is characterized by a wide range of diverse perspectives. While the analysis of the CSR‐development nexus in Africa has been particularly insightful, there is often the lack of sufficiently grounded systematically accumulated empirical evidence. However, central to the CSR‐development nexus debate in Africa is the disagreement over the reimagining of the role of business from being the cause to becoming a part of the solution to the problem of underdevelopment in the region. This paper critically examines the CSR‐development nexus literature in Africa and lays bare the controversies that have so far emerged. The article engages with the drivers of CSR, its dynamics and nature within Africa. It then examines the debate of whether or not contextual factors matters for CSR and its relationship with development. Crucially, it identifies differences between proponents of CSR is good for development and those that share opposing views at the conceptual, practical, and discourse levels. The paper concludes by considering the emerging issues and its implications for future research agenda.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".