Interpretive Agency, Change, and the Role of Individuals in UN Peace Operations
Bibliographic record
Abstract
Much of what peacekeepers do on a daily basis is best understood through the lens of practice, and practice theorists have begun to explore the process by which practice change occurs in international organizations. Yet practice theory’s focus on tacit knowledge and ‘unthinking’ action raises important questions about agency. How can individual peacekeepers change their daily practices amid the myriad structural factors that dispose them to act in particular ways? Do changes in peacekeeping practice require conscious reflection on their part? Drawing on evidence from UN missions in Sierra Leone, Côte d’Ivoire, and the DRC, I argue that changes in practice often occur through interpretive agency as individuals determine what rules require in practice. Interpretive agency takes two distinct forms: practical agency and reflective agency. Reflective agency involves conscious deliberation, while practical agency occurs when peacekeepers make unthinking adjustments to existing practices. This article identifies conditions that are conducive to each type of interpretive agency, highlighting the impact of crisis, bureaucratic hierarchy, and the role of specialized knowledge. These findings improve our understanding of how individual agency shapes UN peace operations, while also contributing to wider debates about change and continuity in global governance.
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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.076 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".