Bibliographic record
Abstract
Abstract Largely uncredited in public media and academic literature, the United Nations has used armed force frequently in the Democratic Republic of the Congo (drc), probably more than in any other UN peacekeeping operation. Though unheralded, this saved lives and protected cities and towns. However, attacks on civilians in drc are so frequent and widespread that many times the mission has been unable to respond in a timely fashion. To save more lives and gain trust in the local population, a much greater UN effort is needed to support robust measures, with more resources, determination and accountability (for inaction as well as action), even as “donor fatigue” sets in for a mission that has been operating since 1999. Still, it is important for peacekeeping as a whole to recognize and learn from cases of use of force against Congolese illegal armed groups (iag s), like the adf, cndp, fdlr, frpi, and M23. These cases show some remarkable successes, including removing some major poc threats, fracturing rebel groups, increasing UN deterrence, and enhancing the rule of law in the still untamed “Wild East” of the immense African country.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".