The Main Actors of the Negotiations
Bibliographic record
Abstract
The main actors in the negotiations were United Nations staff; governments, acting individually and in groups; the officials of the negotiating bodies known as the Bureau; and nongovernmental organizations (NGOs), acting individually and in groups. The International Law Commission had done the most recent drafting and analysis at the United Nations on an international criminal court. Most NGOs and individuals participated in the Ottawa Process through the International Campaign to Ban Landmines, a large international coalition with the single goal of implementing a worldwide landmine ban. Civil society was organized into an effective coalition, whose leader, Jody Williams, later won the Nobel Prize for Peace. Most NGOs at the Preparatory Committee organized their activities through the Coalition for the International Criminal Court (CICC). The CICC grew from thirty NGOs at the beginning to roughly eight hundred in Rome, from all regions of the world and sectors of society. Keywords:Bureau; civil society; ICC negotiations; international criminal court; nongovernmental organizations (NGOs); Ottawa Process; United Nations
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.012 |
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".