Uncovering the Legitimacy of Possible Causes of Conflict in African States
Bibliographic record
Abstract
There has been prominent conflict and intense violence throughout African countries in the past and recent years. This paper will present research regarding the effectiveness of proposed causal mechanisms contributing to the 1994 Rwandan genocide. The plausible causes which may have led to the escalation of conflict in Rwanda during the 1990’s will be discussed. However, a key concept which seemed to lack further analysis when discussing the origin of conflict in Rwanda was the “why” aspect. As my research discusses, there has been similar causal mechanisms outlined and prevalent among various case studies in Africa. Therefore, an imperative question to ask is: Why has the intensity of violence differed between certain African countries that share the existence of similar causal factors? Specifically, I focus on the effectiveness of Belgian colonialism as a contributing factor to the Rwandan genocide and the lack of legitimacy of primordial classification (traditional and static claims depicting similar characteristics which are shared among groups and people). I compare the effects of these possible causes by analyzing the case studies of Rwanda, Burundi, and the Democratic Republic of Congo, in an attempt to explain the differences in the levels of violence witnessed in all three countries which were significantly affected by Belgian colonialism and ethnic classifications of people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".