Smothering the Embers of Conflict in Darfur: Transnational Oil Politics
Bibliographic record
Abstract
It is not an easy feat to identify the origins of conflict in Darfur. It is a conflict so complex that one scholar can blame global warming while another blames a “genocidier” in Sudanese President Omar al-Bashir, and both have solid evidence for their case. The obvious factor that led to the Darfur conflict is past conflict in Sudan. The embers of past conflict were still glowing hot in Sudan when the Comprehensive Peace Agreement (CPA) was signed between North and South in 2005 to end the civil war. Interestingly, the final years of civil war and the first years of conflict in Darfur coincide with increased oil development in Sudan. The issue of oil and conflict in Sudan is important because it involves the actions of corporations, investments of ordinary citizens around the world and the policies of governments. Part of the policy dialog surrounding Darfur and the lack of government-guaranteed human security relates to Sudan’s oil revenues and their connection to military spending. Certain NGOs have endeavoured to draw a direct link between increased oil revenues and military spending in Sudan, urging investors to divest from certain companies to alleviate suffering and conflict. Queen’s took steps last year to drop investments in certain Chinese oil companies because of their involvement in Sudan. This presentation will: examine the link between oil revenues and military spending in Sudan, explore the efficacy of divestment and other economic sanctions and draw some conclusions on the role of corporations (and their investors) in conflict zones.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".