‘Living between Two Lions’: Civilian Protection Strategies during Armed Violence in the Eastern Democratic Republic of the Congo
Bibliographic record
Abstract
This article examines how civilians assess, negotiate with, and in some cases deceive armed actors in the eastern Democratic Republic of the Congo (DRC). It demonstrates that civilians not only navigate the precarious and unpredictable conditions within armed conflict, but also exploit these conditions to improve their security situations. The ‘self-protection’ strategies analysed aim to prevent, mitigate and confront violent threats that civilians encounter in their daily lives. This article argues that civilian self-protection strategies are especially prevalent in contexts marked as ‘no peace – no war’. Characterised by prolonged and low intensity violence, ‘no peace – no war’ contexts shape civilian self-protection strategies in three ways. First, civilians often develop a sophisticated understanding of the actors involved and the patterns of violence that unfold. Second, civilians often learn what particular strategies are most likely to be successful, typically through trial and error. Third, civilians have often become sceptical and cynical about international actors and activities. Understanding what actions civilians take to protect themselves, their families, and their communities is critical for the international community's role in peacemaking and peacebuilding.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".