The ‘Governance Gap’, or missing links in transnational chains of accountability for extractive industry investment
Bibliographic record
Abstract
This article seeks to address the urgent need for emerging-market economies to more firmly tackle the corporate social responsibility (CSR) of their overseas enterprises in Africa. Focusing on Africa's mining sector, it analyses complementarities among: international guidelines for multinational enterprises (Section 2); domestic management norms from countries of origin (Section 3); and African countries’ legal frameworks for CSR (Section 4). Three emerging-market countries are investigated (China, India and South Africa), while Canada -- a ‘traditional’ investor long engaged in mining in Africa -- is used as a ‘benchmark’ for assessing whether the CSR characteristics of emerging-market companies differ from those of more ‘experienced’ investors. The article has two aims. First, to build toward a framework that could help emerging-market investors engage in mutual learning and systemize their CSR approaches in accordance with African host government requirements. And secondly, to shed light on the chains of accountability stretching from governing bodies in countries of origin, to company operations in Africa. This analysis reveals a clear ‘governance gap’ between CSR constraints imposed on companies operating within their countries of origin, and the more lenient standards to which companies investing overseas are held. Section 5 and the Conclusion investigate mechanisms for bridging this ‘gap’.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.006 |
| 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".