'The prison should not be an island' : the role of civil society in post-conflict correctional reform in Rwanda
Bibliographic record
Abstract
Following the 1994 Genocide, the Rwandan government was faced with an unprecedented crisis within its prison system with over 120 000 prisoners being held in conditions that could only be described as inhumane. Over the past 20 years, conditions have improved through various government national unity and reconciliation and correctional reform initiatives, but what role did civil society play? This article begins by situating correctional reform and civil society within the larger peacebuilding rule of law and transitional justice framework before outlining the Rwandan context. A qualitative exploratory approach is then utilised to identify the role of civil society in post-conflict correctional reform in Rwanda. Twenty-two semi-structured interviews were conducted with government as well as international and local NGO officials involved in correctional reform in Rwanda since 1994. Participant interviews revealed that correctional reform activities conducted by civil society in Rwanda fell within three areas: peacebuilding and reconciliation; monitoring and advocacy and operational partnership and support. The importance of civil society's involvement in post-conflict correctional reform in Rwanda was also identified; including that Rwandan civil society shares ownership in the aftermath of the genocide, such as the reintegration and rehabilitation of those that caused the harm. In addition, challenges and best practices are identified as well as policy and practice implications.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".