Bibliographic record
Abstract
In this paper the March 2000 report of the Panel of Experts of the UN Security Council Angola Sanctions Committee is revisited by the author, who served as the Chairman of this Panel. It is shown that the effects of the report are still visible. Some of the "techniques" of the Committee and its Panel are put forward as contributors to its relative success. Among these are the role played by its dynamic Chairperson, the Canadian UN Ambassador Robert Fowler; the use of media and general transparency in its work; its goal orientation, rather than a legalistic, punitive approach; high evidentiary standards and strict and clear reporting; and luck, in as much as the simultaneous successful offensive of the armed forces of the Angolan government helped bring forth new information. It is argued that Sweden, as a country with a relatively high level of expertise, experience and knowledge, and with its good standing internationally and particularly in the UN could more actively take part in efforts to continue to develop the instrument of smart sanctions. It is further suggested that efforts could be made to strengthen the capacity not only of the UN centrally but also of regional and sub-regional organizations such as the AU and SADC in Africa to propose, design, and follow-up on sanctions regimes.
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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 | 0.070 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.031 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 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".