The extractive industry and human rights in Africa: Lessons from the past and future directions
Bibliographic record
Abstract
Although the extractive industry has contributed to the socio-economic development of many African countries, it has also led to incidences of human rights violations in many rural communities. However, the use of an evidence-based approach to search, locate, explore and synthesize the literature systematically in order to understand the nature and pattern of human rights violations within the extractive industry remains limited. Consequently, this study employs the systematic review method to determine the nature and drivers of human rights abuses within the extractive industry in Africa. Of the 791 articles retrieved from the search of the databases, 58 articles met the inclusion criteria and were included in evidence synthesis. Based on the thematic analysis conducted on the articles that met the inclusion criteria, we find that human rights abuses tend to be associated with the violation of economic, social, and cultural rights, tensions over land ownership, the loss of livelihood, and community marginalization. We conclude the study with some policy implications and suggest avenues for future research.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".