Bibliographic record
Abstract
Abstract After years of uneasy interactions between successive administrations of the United States and the International Criminal Court (ICC) — ranging from apathy to indifference to tacit utilitarian engagement behind the scenes — the administration of President Donald Trump unleashed the most intense aggression against the Court, marked by direct coercive measures against the Court’s officials. This followed relentless prior threats of such actions and promises to do more harm. Since other international institutions including the World Health Organization, the World Trade Organization, and the Human Rights Council have also been on the receiving end of rough treatment from Mr Trump, it is evident that the experience of the ICC under his administration was not entirely unique, although the particular circumstances may have been so. The new administration of Mr Biden has signalled an intention to reset US foreign policy — and has started this process — by reversing the more aggressive aspects of Mr Trump’s stance. In relation to the ICC, this change should go beyond merely reversing Mr Trump’s coercive measures. It should include positive support for the Court, given America’s leading role in inspiring its creation and the Court’s raison d’être which remains the cause of all humanity.
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 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 | 0.002 | 0.003 |
| 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 | 0.000 |
| 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".