The role & influence of natural resources in civil wars in Africa: examples from the Liberian and Sierra Leonean civil wars
Bibliographic record
Abstract
The objective of this thesis is to test and analyze the proposition of whether and to what extent natural resource interests have been fundamental in either causing, fueling or prolonging civil wars in Africa. The study focuses on examples from the Liberian (1989-96 and 1999-2003) and Sierra Leonean civil (1991-2002) to better understand the workings of armed rebellion and the role of natural resources. These two conflict situations were chosen because of their international/transnational contexts and because of the large number of actors involved in either their management or resolution- (UN, ECOWAS, NGOs, and other International Non-Governmental Organizations). More importantly both nations are resource rich. This study examines closely the relationship between the management of natural resources (with focus on lootable natural resources) and armed conflicts and makes policy recommendations aimed at reducing the risk of resource-related violent conflicts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".