‘This is how we survived’: Civilian agency and humanitarian protection
Bibliographic record
Abstract
Abstract The security of civilians in contemporary conflicts continues to tragically elude humanitarians. Scholars attribute this crisis in protection to macro-structural deficiencies, such as the failure of states to comply with international conventions and norms and the inability of international institutions to successfully reduce violence by warring parties. While offering important insights into humanitarianism and its limits, this scholarship overlooks the potential of endogenous sources of protection – the agency of civilians. On the basis of a case study of northern Uganda, we identify and discuss several civilian self-protection strategies, including (a) attempts to appear neutral, (b) avoidance and (c) accommodation of armed actors, and argue that each of these is shaped by access to local knowledge and networks. We illustrate how forced displacement of civilians to ‘protected villages’ limited access to local knowledge and, in turn, the options available to civilians in terms of self-protection. Analyses of the intersections of aid and civilian agency in conflict zones would afford scholars of humanitarianism greater explanatory insight into questions of civilian protection. The findings from our case study also suggest ways in which aid agencies could adopt protection strategies that empower – or at least do not obstruct – the often-successful protection strategies adopted by civilians.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.060 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".