Natural Resource Conflicts as a Struggle for Space: The Case of Mining in Tanzania
Bibliographic record
Abstract
Natural resource extraction in Africa has been characterised by conflicts between large scale and small scale miners on the one hand and large scale miners and the communities on the other. In some countries such as Sudan, Democratic Republic of Congo, Angola, Mozambique, Sierra Leone and Liberia, natural resources have bred political instability and civil wars. A great deal of academic discourse on resource conflicts in Africa focuses on greed, corruption, political struggles for state capture and control over resources, economic liberalisation policies for attracting foreign investors and creating conducive climate for them to invest their capital in natural resource extraction, and foreign forces. While recognising the significance of the above approaches in explaining resource conflicts in Africa, this paper aims at explaining resource conflicts as a struggle for space between the communities, artisanal and small scale miners and large scale foreign mining corporations. The paper, therefore, argues that natural resource extraction conflicts in Africa can well be understood if we approach them as a struggle for space. Data for this paper are drawn from secondary sources including academic literature, government reports, media reports and internet sources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".