Bibliographic record
Abstract
Purpose The purpose of this paper is to sets out to highlight the role and evolution of corporate social responsibility (CSR) in Africa's extractive industry. Through the discussion and analysis of the history of CSR in Africa, best and worst practices in the industry, corporate objectives and business ethics, as well as the use of CSR as a tool for corporate citizenship and sustainable development, this paper works to develop a more concise understanding of the role that CSR has come to play in the African extractive industry. Design/methodology/approach Through the discussion and analysis of the history of CSR in Africa, best and worst practices in the industry, corporate objectives and business ethics, as well as the use of CSR as a tool for corporate citizenship and sustainable development, this paper works to develop a more concise understanding of the role that CSR has come to play in the African extractive industry. Policy recommendations are also presented to the public and private sectors on how to mend the gaps and complexities of CSR and move forward with CSR practices in a sustainable manner. The paper draws solely on the use of secondary sources to achieve these results. Findings Throughout the research and analysis, this paper argues that while CSR has evolved in the last few years and become more relevant in the extractive industry in Africa, there is still much work to be achieved, especially in the areas of capacity building, both physically and structurally. Policy development and implementation as well as greater accountability of, and cooperation between, governments and corporations is necessary to achieve long‐term sustainability. Originality/value Policy development and implementation as well as greater accountability of, and cooperation between, governments and corporations is necessary to achieve long‐term sustainability. Such recommendations are of imminent importance for the continent's economic development, given the resource boom currently taking place across Africa.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.055 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".