Bibliographic record
Abstract
Abstract UN peace operations increasingly deploy police forces and engage in policing tasks. The turn to ‘police peacekeeping’ has generally been met with enthusiasm in both academic and policy circles. Policing is often understood to provide a more civilian instrument of intervention, and rebuilding local police forces along democratic, liberal lines is seen as a prerequisite for a successful transition towards peace and stability. This book questions this optimistic reading of police peacekeeping. It demonstrates that the logic of policing leads to the depoliticization of conflict and the criminalization of those who are deemed to threaten not just public order but social order, authorizing violence against them in the name of law enforcement. The book proposes a new way of studying peace operations that focuses not on their success or failure, but on how they allow people and ideas to circulate transnationally. It shows that peace operations act as a point of cross-fertilization for the creation and transmission of policing discourses and practices globally. In so doing, these missions contribute to (re)producing social orders that are based on the exclusion of often racialized, socio-economically marginalized populations, both ‘domestically’ (in countries of intervention) and ‘internationally’ (in troop contributing countries). The book contributes to critical understandings of police power that show that police forces were never meant to protect all equally. Drawing on interpretive, feminist, and postcolonial methodologies that emphasize relations, processes, and situatedness, the book’s in-depth study of UN intervention in Haiti shows how a single site can help illuminate global processes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.127 | 0.038 |
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".