Bibliographic record
Abstract
In her paper, "Monénembo's L'Aîné des orphelins and the Rwandan Genocide," Lisa McNee discusses Tierno Monénembo's L'Aîné des orphelins, a novel that offers a double discourse and a dual memory of the genocide that took place in Rwanda in 1994. McNee argues that L'Aîné des orphelins presents us with an extraordinary kind of fictional testimonial to genocide. Although Monénembo is not from Rwanda and did not participate in the tragedy, only someone who has paid the price of the clarity needed to distinguish between good and bad faith could have written a novel like L'Aîné des orphelins. Monénembo's characterization of Faustin as a young man who feels guilty without reason is plausible, given what we know of victims of abuse and their reactions, or those of survivors of other humanitarian catastrophes. One can only speculate about Monénembo's own experiences as an exile. In Monénembo, McNee proposes, we have a rare example of an intellectual who accepts the burden of a certain complicity, the complicity of those who did not speak out or intervene at the time of the genocide and who does not flinch at the cost of confronting that complicity. Of course, the Rwandan genocide recalls the shadows of other genocides and the reflections that others have shared may help us to better understand the Rwandan genocide and its aftermath.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".