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Record W2943904006 · doi:10.22215/etd/2015-11841

Child Soldier Stories and the American Marketplace

2015· dissertation· en· W2943904006 on OpenAlexaff
David Mastey

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsCarleton University
Fundersnot available
KeywordsBlameNarrativeContext (archaeology)NormativeMemoirHistorySociologyLiteratureGender studiesPolitical scienceAestheticsPsychologyArtLawSocial psychology

Abstract

fetched live from OpenAlex

This project offers an analysis of the child soldier story genre of literary writing in the context of Africanist discourse in the U.S. It defines the genre as encompassing fictional and non-fiction narratives that depict child soldier protagonists in Africa as written by African authors.It examines nine of the most popular and critically-acclaimed works through their predominant themes and concludes that the genre ultimately makes a harmful contribution to how the African continent is popularly understood in the U.S.The first chapter defines the genre as it has been constructed by the American publishing industry.It places child soldier stories within a larger marketing category known as misery literature and explains how they are taken to inappropriately represent everyday life in Africa.enough.You have not only made this project better through your thoughtful and helpful critiques, but your encouragement made the whole thing seem possible and worthwhile.

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)0.002
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0140.013
Scholarly communication0.0090.005
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.014
GPT teacher head0.351
Teacher spread0.337 · 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
Published2015
Admission routes1
Has abstractyes

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