Re-education camp as a spatial governance form: Separation, structure, and staging in Rwanda’s <i>ingando</i> camps
Bibliographic record
Abstract
Compared to other forms of encampment, contemporary re-education camps do not draw much comparative work in sociology and politics. Even less is known about the governance of such camps, and the ways in which unique spatial aspects of encampment are meant to facilitate the intended social and ideological transformation which is their hallmark. Taking post-genocide Rwanda’s uniquely widespread system of ingando “solidarity” camps as its case study, this article argues that three aspects of spatial governance in particular—separation, (hyper)structure, and staging—are central to the modus operandi of re-education camp as a technology of transformative power. The ingando system was born amid a crisis of citizenship in the wake of mass atrocity and shaped by a rebel-turned-ruler elite aiming to reconstruct citizenship sifted through the lens of glorified militarism. The ingando camps are not only about learning together, but their remoteness is meant to unsettle, their structure is meant to foster discipline, and their staging and experiential aspects are meant to simulate values of unity as cohesion and submission to a broader goal.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".