Bibliographic record
Abstract
This dissertation interrogates the newly prominent figure of the child soldier in African literature. I examine a number of recent texts narrating the child soldier experience, both memoir (Ishmael Beah, Emmanuel Jal, China Keitetsi, Senait Mehari, Grace Akallo, Tchicaya Missamou, Niromi de Soyza) and fiction (Uzodinma Iweala, Ahmadou Kourouma, Emmanuel Dongala, Chris Abani). The anthropologist David Rosen argues that the contemporary Western humanitarian narrative often makes an automatic assumption of innocence based on age that is not necessarily applicable in non-Western cultures. The danger of imposing such Western frameworks on non-Western cultures is that it risks engaging in the same colonial tropes of paternalism towards the native â childâ that were used to maintain dominance over colonized populations. Yet the hunger for narratives that portray the child soldier as an innocent victim who eventually is rescued and rehabilitated, as well as the fact that child soldier narratives are almost purely an African genre (even though there are substantial numbers of child soldiers in Asia, South America and the Middle East) suggests the kind of Orientalism that Edward Said warned us against: a desire to see Africa specifically as a place of violence and lost innocence that can be redeemed through Western intervention. \n\t This study takes a comparative approach, contextualizing the current literary trope of depicting the child soldier as lost innocent by comparing these contemporary narratives to a range of other texts. Chapter One examines the striking parallels between child soldier narratives and antebellum American slave narratives. Chapter Two juxtaposes child soldier narratives to the very different portrayal of South African youth involved in the militarized anti-apartheid movement. Chapter Three compares child soldier narratives to three texts narrating the experiences of young adult soldiers in the Zimbabwean war of liberation. Chapter Four questions why the child soldier is almost invariably imagined as African, while analyzing the one real exception to this rule, Niromi de Soyza's Tamil Tigress. Ultimately, through its examination of literary representations, my dissertation exposes the category of (African) child soldiers as highly problematic, allowing us to reconsider implicit myths of childhood and human rights.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.077 | 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 teacher head, 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".