Bibliographic record
Abstract
Abstract National reconciliation is a vague and 'messy' process. In post-genocide Rwanda, it presents special difficulties that stem from the particular nature of the Rwandan crisis and the popular participation that characterized the Rwandan atrocities. This article outlines the main approaches being used in Rwanda to achieve reconciliation, highlighting some of the major obstacles faced by these institutions. It then goes on to argue that certain 'Silences' are being imposed on the reconciliation process, including the failure to prosecute alleged RPA crimes, the lack of debate on, and the instrumentalization of, Rwanda's 'histories', the collective stigmatization of all Hutu as génocidaires, and the papering over of societal cleavages through the 'outlawing' of 'divisionism'. The role economic development can play in the reconciliation process is also discussed. Given the Government of Rwanda's central role in the reconciliation process and its progressive drift towards authoritarianism, the article ends with a reflection on the worrisome parallels between the pre and post-genocide socio-political contexts.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".