Risky Business: Organizational Challenges in International Support for Civilian Self-Protection
Bibliographic record
Abstract
Over the last decade, a range of international actors has moved away from direct forms of intervention to protect civilian populations in favor of “bottom-up” approaches that emphasize external support for civilian self-protection (CSP). While this indirect action is often perceived to be less costly, more legitimate, and potentially more effective, we argue that external support for CSP is a “risky business” that presents a significant dilemma for international governmental and nongovernmental organizations. Drawing on literature from sociology, economics, and civil war studies, we conceptualize and categorize the risks of unintended consequences that could accompany external support for CSP and suggest why they are likely to arise in this context. We then empirically explore whether and how these risks manifest with an in-depth study of four purposively selected organizations supporting CSP in their programming. We also assess whether and how organizational type matters for the prevalence of these consequences and how the risk of their occurrence is managed. Our analysis shows that, across a range of conflict settings, all four organizations encountered unintended consequences of three main kinds: increased vulnerability and insecurity for local communities, challenges to organizational mandates and values, and strained relations with key protection stakeholders. International actors supporting CSP thus confront the dilemma of seeking to enhance their effectiveness and legitimacy by “localizing” protection, but potentially create new challenges and perverse effects and/or compromise their organizational identity in the process. While this dilemma is inherent in all external protection assistance, our study highlights the importance of actor embeddedness: organizations that are more proximate to the communities they work with are in a better position to minimize these unintended consequences and manage the risks associated with supporting CSP. These findings contribute to ongoing debates about civilian protection in comparative politics, international relations, and humanitarian studies, but also offer concrete insights for practitioners engaged in support for CSP. More broadly, our study could have implications for other policy areas where the legitimacy of so-called top down approaches is being questioned and where these approaches are giving way to the empowerment of local actors and processes.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".