Navigating Crisis and Chronicity in the Everyday: Former Child Soldiers in Urban Sierra Leone1
Bibliographic record
Abstract
The aftermath of war is typically referred to as ‘post-conflict’, often insinuating a stage of relative calm following a period of armed violence, upheaval and strife. However, the assumption that the post-war context brings forth peace, prosperity and stability negates the reality that conflict, violence and poverty may become embedded in the post-war social fabric. Following its decade long civil war, Sierra Leone continues to contend with a political, social and economic reality marked by widespread poverty, violence, and devastated health and social service systems, highlighting that for many, ‘crisis’ has in fact become chronic and endemic in the post-war period. Drawing on interviews with 11 former child soldiers living in an urban settlement, this article underscores the blurred distinction between periods of war and peace. Moreover, using the concept of social navigation, the paper explores the strategies the youth deliberately and tactfully employed in negotiating a volatile post-conflict terrain. Their narratives reveal their active, rather than passive, efforts in fostering their own social, economic and physical wellbeing in light of ever-changing, and unstable circumstances.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".