Bibliographic record
Abstract
Bureaucracy refers to the administration of care and control, and a growing body of literature and theory seeks to directly address the role of bureaucracy in peace and conflict settings given its inextricable presence in violence and peacebuilding endeavors. As an interdisciplinary topic of study, bureaucracy is variously examined as a tool for processing and provisioning for social needs, a way to obscure power asymmetries, and a producer of barriers to accessing needed services. Because of these factors, the relationship and role of bureaucracy within peace and conflict contexts merit inclusion in any comprehensive compilation of key concepts. In this chapter, the authors examine the historical roots of the term and its usage in peace and conflict studies, revealing how bureaucracy functions in systems of power and control, ranging from the extraordinary to the mundane. The practical importance of applying concepts from bureaucracy research is evidenced via case studies from diverse micro- and macro-level conflict and peace settings, including human trafficking, transitional justice in Canada, and refugee and immigrant processing. Finally, critical examination of the field suggests the need for further research in bureaucratic successes and some types of complex bureaucratic relationships.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".