ETHNIC PLURALISM, SOCIAL JUSTICE AND INTEGRATION POLICY IN POST CONFLICT RWANDA
Bibliographic record
Abstract
Like every war ravaged country, the Republic of Rwanda is reawakening to grapple with the challenges of post-conflict reintegration and transformation. To scholars and observers of the trend, Rwanda is recuperating at a very high speed due to socio-economic reforms and the apparent commitment of the Government of the country to rebuild a new Rwanda from the rubbles of the devastation that greeted the 1994 genocide. Expectedly, the Rwandan government generated laws and codes which govern social interaction – former ‘enemies’ that must co-habit. There is public ban on all divisionism tendencies. In Rwanda there should be no ‘Hutu’, ‘Tutsi’ or ‘Twa’. All are Rwandans. Indeed, there are sanctions against defaulters irrespective of their nationalities. The drive for identity reconstruction is fierce and the government of Rwanda is determined to obliterate the ethnic ideologies which it believes, reinforced the 1994 Genocide against the Tutsi in Rwanda. However, the questions to ask are: will suppression of ethnic identity effectively obliterate natural affinity for group relations and the right to cultural identification and association? How does the government policy against sectarianism help in the reintegration programmes in Rwanda particularly the traditional judicial option called the Gacaca? This paper seeks to address these questions based on the data collected from a field-work conducted in Rwanda in 2011 and from the observations of scholars of ethnicity and the Rwandan Crisis.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".