Bibliographic record
Abstract
Several challenges arise in responding to atrocity crimes in contemporary practice. First, there is not the same proactive vision for justice in the U.N. Security Council as existed in 1993 and 1994. Second, reflecting upon the practice of the International Criminal Tribunal for the former Yugoslavia and recent controversial judgments, the question looms whether judges properly evaluate how mass atrocity crimes occur within the particular characteristics of the overall situation or overall conflict. Third, the great value of international criminal tribunals and internationally-created hybrid tribunals is that they create a clear public record of events that, through the rigorous investigation and prosecution of atrocity crimes, rebut attempts at denial or revisionism by politicians and extremists. Further, a new paradigm in international affairs should be formulated, one that compels effective, timely, and significant multilateral responses directly aimed at lawless forces engaged in atrocity crimes and bold enough to act swiftly with lawful justification even in the absence of Security Council authorization thwarted by the veto power.
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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.075 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.032 | 0.038 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".