Surviving the Slaughter: The Ordeal of a Rwandan Refugee in Zaire. By Marie Béatrice Umutesi. Wisconsin: University of Wisconsin Press, 2004. 258pp. £14.95. ISBN 0 299 204 944 pb.
Bibliographic record
Abstract
How might we come to know the abasement of daily life to the point where plastic sheeting becomes pivotal to one's existence? In Surviving the Slaughter: The Ordeal of a Rwandan Refugee in Zaire, Umutesi renders the seemingly endless traumas of the Rwandan genocide into a readable testimony of the devastation and resistance of those who fled, often many times over, and the extreme violence, murder, starvation and betrayal that was Rwanda's catastrophe. The impotent response of the international community is depicted by the author as the depth of the mundane, explained beyond the journalistic accounts, academic analyses and human rights reports to provide the reader with a sharp critique of how the mistakes of the international community, aid agencies and intergovernmental organizations further burden destitute, yet resourceful, refugees. Writing as an exile living in Belgium, Umutesi recounts her experiences and those of the people around her through the...
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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.
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".