Sustainability and global value chains in Africa: Introduction to the Special Issue
Bibliographic record
Abstract
The challenges and opportunities facing African organizations reflect a long history of tensions, tragedies, triumphs, and accomplishments in relationships across continental boundaries. For example, Africa has long been a source of critical minerals and other raw materials that are integral to a wide range of global industries, but scholars of management have not integrated an understanding of Africa's role in global commerce fully in research on international exchange. Perhaps most importantly, scholarship in the field of management has not addressed the extensive opportunities for the development of innovative ideas, capabilities, capacities, inventions, and breakthroughs that would be made possible by international investments in human development and human capital on the continent. Resolving African problems and pursuing African opportunity requires renewed commitment by management scholars to this agenda. In this introductory article, we focus particularly on the structure of relationships across continental boundaries through global value chains (GVCs) and the role political and corporate sustainability conversations and initiatives play. We also seek to explore their implications especially for African organizations that simultaneously pursue economic growth and constructive social and environmental impact. We conclude with a framework for further study by management scholars on these important issues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".