National Staff and the Protection of Civilians in UN Peacekeeping: Facilitative and Direct Agency
Bibliographic record
Abstract
Although far less visible than international peacekeepers, locally recruited ‘national staff’ play a vital role in UN peacekeeping and exercise agency with respect to core mandated tasks, including the Protection of Civilians (PoC). National staff bring critical local knowledges and communicative access advantages into missions but hold subordinate status within them. To fully capture their agency, I draw on feminist scholarship to conceptualize ‘facilitative agency’ (enabling others to achieve otherwise unavailable impacts) and highlight its importance in UN peacekeeping alongside the ‘direct agency’ of engaging local counterparts to produce outcomes. Feminist scholarship also helps surface the tensions surrounding the distribution of facilitative and direct agency in bureaucratic hierarchies. Drawing on 203 interviews in four UN missions (MINURSO, MINUSCA, MONUSCO and UNFICYP), I show that national staff often have facilitative agency and may also have direct agency. I suggest that the type(s) of agency national staff have are shaped by their position type, work location and the specialization and/or transferability of their expertize and access, but also by international peacekeeper gatekeeping. I substantiate this argument by providing examples of national staff facilitative and direct agency for physical and political PoC and noting the conditions creating and limiting these types of agency.
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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 | 0.008 | 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 | 0.011 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| 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".