Comparative camp governance: Power, autonomy, and pluralism
Bibliographic record
Abstract
How, by whom and to what effects are camps governed today? Despite persistent critiques, camp institutions remain a resilient and versatile apparatus of power globally. Yet there is only limited conceptualization of camps and the multi-scalar governance they operate within from a comparative perspective. This special issue remedies this by looking at governance of five different types of camps: prison camps, detention camps, (re)education camps, refugee camps, and relocation camps. In all these seemingly contrasting iterations, we argue that contemporary camp institutions (from Guantanamo to refugee camps) are deployed ultimately as an order-making apparatus. Camps deploy plural governing techniques for this purpose, ranging from material, spatial, and high-tech to ideological and experiential. Nevertheless, it is argued that these institutions represent a self-contained reality and an autonomous order that is distinct from the broader objectives and planning that initially established them. Part of this order demonstrates a diverse range of resistance mechanisms to dominant governing logics. Overall, we argue contra to the prevailing Agambenian theorization of the camp: Camps are not spaces of exception that reveal the norm but have become an expected norm in contemporary governance, and they are not a priori or ultimately spaces of exclusion, but instead apparatus of desired forms of incorporation into the dominant socio-political order, whether state or non-state.
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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 | 0.006 |
| 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 | 0.044 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".