Bibliographic record
Abstract
Sometimes I think that I am a good person. But then how can I say that I'm a good person after all the horrible things that I've done? (Boy) Helping children recover from … their [wartime] experiences and ensuring their long term reintegration into their communities remains a considerable challenge. (United Nations 2007) While Sierra Leone is slowly recovering from the brutal civil war, remnants of the violence remain apparent throughout the country; the sight of amputees and crushing poverty are only a few of the daily reminders of the brutal violence of the past. In addition, less visible markers of violence inevitably pervade the hearts and minds of all those who lived through the war. For the participants in this study, the remnants of violence are powerful and ever-present, although often concealed and spoken of only in highly selective contexts. As this final chapter will illuminate, the narratives gathered from the young people reveal some of the post-war opportunities and challenges for former child soldiers. The chapter addresses some of the new and figurative battlefields that exist at the war's end – for both the child soldiers in this study, as well as the many institutions working on their behalf. The chapter begins by summarizing the link between structure and agency in the process of making and unmaking, underscoring the utility of Giddens' concepts in enabling a greater understanding of the wartime and post-war lives of child soldiers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.208 | 0.044 |
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".