After Atrocity: Optimizing UN Action Toward Accountability for Human Rights Abuses
Bibliographic record
Abstract
It is a great honor for me to be here to deliver the John Humphrey Lecture. Humphrey led one of those lives within the UN that shaped what the organization has become today—as one of the first generation of UN civil servants, he was to human rights what Ralph Bunche was to peacekeeping, or Brian Urquhart to UN mediation. To read his diaries, so beautifully edited by John Hobbins, is to see a world that has in many ways vanished, a nearly entirely male club, mostly of Westerners, that hammered out new treaties and mechanisms over fine wine and cigars in many a European tourist destination—as well as lesser sites like Lake Success, New York—but also where the foundations were laid for projects still central to the UN’s work. For without Humphrey’s Division of Human Rights, there would be no Centre for Human Rights, and then High Commissioner for Human Rights, with his vast secretariat in Geneva and now field office presences around the world. Yet at the same time, I’m here today to talk about a topic that was not the targets of John Humphrey’s energies – the UN’s role in holding individuals accountable for human rights atrocities. Individual accountability is a broad concept. It encompasses a range of processes that put the spotlight on individuals for human rights abuses they have committed, mete out consequences to them, and address in some meaningful way the legacy of the abuses on victims. Humphrey’s cause, from the drafting the Universal Declaration on, was to create norms and mechanisms for holding states responsible for violations of human dignity. Even though the Nuremberg trials had recently concluded, indeed some of the lower-level trials were being held while the UHDR was being negotiated, creating norms and mechanisms for holding individuals accountable was not the priority, probably because it was seen as both less important and far more challenging than holding states accountable. In reviewing his many writings about the UN and human rights, I found that he barely wrote on the subject. His 1989 book No Distant Millennium had a brief treatment of the treaties on slavery, genocide, and torture, each of which makes the banned conduct a crime for which individuals must be prosecuted, but he really focused on those treaties for their new obligations on states. Professor McDonald’s long tribute to Humphrey in the 1991 Canadian Yearbook of International Law mentions it only once, citing a 1988 speech in which Humphrey called for the creation of a court of international criminal justice. So my talk today is about a project whose importance for the UN became apparent only in Humphrey’s final years and in the two decades since his death.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".