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Record W2479472802 · doi:10.1017/cbo9780511676475.006

‘Becoming RUF’: The making of a child soldier

2010· book-chapter· en· W2479472802 on OpenAlexaff
Myriam Denov

Bibliographic record

VenueCambridge University Press eBooks · 2010
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMilitary History and Strategy
Canadian institutionsMcGill University
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

I am beginning with the young. We older ones are used up … We are rotten to the marrow. We have no unrestrained instincts left … But my magnificent youngsters! Are there finer ones anywhere in the world? … Look at these young men and boys! What material! With them I can make a new world. (Adolf Hitler, cited in Rempel 1989, p. 2) The rebels attacked my village and I was separated from my parents … [They] threatened to kill me if I made any attempt to run away. I didn't want to die so I joined them. I was afraid of being around these dangerous men with all kinds of weapons … I had no mom, no dad, sister or brother … I was alone for the first time in my life. (Boy) The making of an RUF child soldier is undoubtedly a complex and multi-faceted process that occurred gradually over an extended period of time. This chapter sheds light on the important context in which this process of RUF militarization began, and the means through which the transmogrification of disoriented youngsters into often obedient and militarized members of the RUF deepened. Drawing upon participants' narratives, the chapter explores boys' and girls' experiences with recruitment and the militarized training they received.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0110.012
Scholarly communication0.0040.005
Open science0.0010.005
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0120.003

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.

Opus teacher head0.033
GPT teacher head0.232
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooksSame topicMilitary History and StrategyFrench-language works237,207