Conflict and the conceptions of identities in the Sudan
Bibliographic record
Abstract
My native Sudan has been ravaged by conflicts over identity and socioeconomic marginalization since independence from Britain in 1956. Elitist debates confined the country’s diverse identities to two dichotomous categorizations: Arabism, associated with Islam and Arabic descent and culture, and Africanism, linked to Christianity, indigenous beliefs and African culture. These polarized views, along with the dominant ideology of the imposition of Arabism and Islam as the basis of national identification, triggered a national identity crisis. This crisis contributed to the escalation of armed conflicts notably the civil war between the North and the South and the current conflict in the Sudan’s Western province of Darfur. This article explores the Darfur conflict which erupted between the central government and liberation groups in 2003, and has been described both as the first genocide of the 21st century and an ethnic cleansing in which the Arab militia are killing the Africans. Using data gathered recently in the Sudan, this article extends the debates on Sudanese identities by showing that the boundaries between Africanism and Arabism are fluid, and by positing multiple identities that resurface as a result of globalization, migration and social ties among ethnic groups. By deconstructing the dominant conceptions of the Sudanese identities, and considering new conceptions about these identities, we can address social dynamics that impact the conflict and take them into consideration when it comes to conflict resolution. Multiculturalism is proposed as a model that could help to accommodate the country’s diverse identities and foster stability.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.032 | 0.050 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".