Bibliographic record
Abstract
As we become evermore aware of the costs and consequences of genocide and various other human rights abuses, the recognition of the need for more effective prevention and intervention strategies also becomes evermore clear. All too often when out- breaks of violence have occurred, the international community has appeared power- less to prevent it and absolutely ineffective when taking steps to stop the violence and the killing. Perhaps the worst contemporary example of this impotence comes from the Democratic Republic of the Congo. Rooted in the destabilizing effects of the 1994 genocide in neighboring Rwanda, the Eastern Congo has been the setting for mass rapes, massacres, and other atrocities since 1995 as various factions and groups have struggled for power and/or resources or have capitalized on the chaos and brutality. Some estimates suggest that more than 5 million people have been killed since the outbreak of hostilities in the mid-1990s.1 This has been the reality there, even though the Congo is the site of one of the longest-standing and largest United Nations peacekeeping missions in existence. Begun in 1999, the United Nations Organization Mission in the Democratic Republic of the Congo (MONUC, renamed MONUSCO in April 2010) has gone from a contingent of about 5,000 troops and 500 military observers to over 20,0000 troops, 700 military observers, 1,000 police personnel, and several thousand assorted other civilian personnel in early 2010.2 Despite this significant international presence, the violence has continued to the present day. In fact, some of the more recent mass rapes and massacres have occurred in close proximity to contingents of the UN peacekeeping forces, which have been unwilling or unable to intervene in these atrocities.3 Keep in mind that these are not always hit-and-run attacks that occur too quickly for a peacekeeping response. In some cases, the assaults lasted for days. Clearly, MONUSCO has not been very effective in preventing the victimization of innocents. Unfortunately, this has often been more the norm than the exception in locations across the globe. The United Nations and the International Community have usually been unable to prevent and unsuccessful in intervening to stop atrocities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".