Awareness in action: MBP students help sexual violence survivors in DRC
Bibliographic record
Abstract
The DRC, formerly Zaire, is a large country in central Africa roughly equal to the size of Western Europe. It is exceedingly rich in natural resources such as gold, copper, diamonds, petroleum and coltan (Columbite-tantalite)—the vital ingredient in cell phones, jet engines and computer chips. The country’s infrastructure, including the healthcare system, was left in tatters after decades of corruption under Mobutu’s dictatorship (1965-1997). By the time his rule ended, the country was so weakened that a year later Rwanda and Uganda, the DRC’s eastern neighbours, launched an attack to gain control over the plentiful natural resources. This fuelled six years of what some have called “Africa’s First World War” (1), involving the armed struggle between the government and numerous rebel groups from both within and around the DRC. Approximately 3.9 million people have died between 1998, when the major conflict started, and 2004 (1, 2). Currently, over 15,000 UN peacekeepers, particularly in the volatile eastern region, are deployed in the DRC under MONUC (Mission de l’Organisation des Nations Unies en Republique Democratique du Congo (3).
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 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".