Bibliographic record
Abstract
From the first day the American Embassy was invaded and its diplomatic staff was seized as hostages by Iran, the United States of America has pursued every legal channel in order to resolve this crisis by peaceful means. Its efforts started with the immediate dispatch of Mr. Ramsey Clark on a mission to negotiate with the government of Iran and were continued in the United Nations through the Secretary General, the Security Council, the U.N. Commission of Inquiry and the World Court. Though one can say that all disputes may theoretically be capable of settlement according to rules of law, it should also be said, as a matter of fact, that international law often has only limited relevance to disputes arising among States. This is so because international legal rules operate within a system which has no general scheme of sanctions and no central organ for the enforcement of international legal rights. States have traditionally utilized coercitive measures short of war in attempting to prevail in disputes with other States. Does this authorize a State to intervene by the use of force for the protection of its nationals abroad ? Even though, from the standpoint of morality, the abortive U.S. rescue operation in Iran may have had sound justifications, any legal justifications that could be put up had to give way before the principle of territorial sovereignty.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".