�Nothing ever dies�: memory and marginal children�s voices in Rwandan and Vietnamese narratives
Bibliographic record
Abstract
Memory is a highly contested notion insofar as it is claimed by the collective (Halbwachs, Young) and deployed within a variety of political and socio-cultural contexts. For Viet Thanh Nguyen, the �true war story� can be told by those who lived through it, thereby wresting power from �men and soldiers� and dominant structures (Nothing Ever Dies, Harvard UP, 2017: 243). Examining the dialectics of remembering and forgetting, this article examines narratives which reclaim memory as a personal and as a collective plea to understand the structural discrepancy at play from the child, who is victim of war. It examines the memoir of a Tutsi refugee child, Moi, le dernier Tutsi (C. Habonimana, Plon R�cit, 2019) and an autobiographical narrative by a Vietnamese refugee in Canada, Ru (K. Th�y, Liana L�vi, 2010), to gauge the extent to which such narratives create their own memorial spaces and in so doing reclaim their marginal memories and centre them, while grappling with the imperative to forget. Ultimately it tests Nguyen�s theory that memory can be just and that in this ethical recoding of memory, the humanity and inhumanity of both sides is underlined.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".