The Scope for Agency and the Role of Individuals in UN Peace Operations
Bibliographic record
Abstract
Peacekeeping, often portrayed as a collective endeavour governed by the United Nations Security Council, is, in practice, deeply contingent on the agency of individuals, who must interpret and implement complex mandates. Academic recognition of the role played by individuals has grown recently, and existing research has gone some way to showing how the effects of individuals can be assessed empirically. Yet work in this area is relatively nascent. Significant gaps remain in our understanding of how micro-level dynamics interact with structural forces, and of the overall scope for individual agency in peace operations. An emphasis on individual agency raises a series of pressing questions, which animate the contributions to this special section. These include questions about the effects of personal attributes, like nationality or level of education, on behaviour; the impact of individuals on strategic culture and operational norms within a mission; individuals’ capacity to bring about change amid structural constraints; and questions about methodological challenges that arise when studying these micro-level dynamics. In addressing these questions, the special section contributes to ongoing scholarly debates and to contemporary peacekeeping practice, embedding the study of individual agency within broader analytical frameworks and exploring its critical role in shaping peacekeeping outcomes.
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 | 0.008 | 0.012 |
| 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 | 0.023 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".