Bibliographic record
Abstract
In 2002 a brutal civil war was raging in Sudan.In an attempt to force Khartoum to start peace talks with the opposition, members of the U.S. Congress discussed imposing sanctions on energy firms working in the African country.The reasoning of the American lawmakers was simple: the threat of sanctions might be sufficient to persuade the Sudanese government, anxious to avoid an exodus of foreign businesses from Khartoum, to negotiate with the rebels.Congress decided that the best way to pile the pressure on Sudan's rulers was to impose innovative measures preventing the global oil companies that did business with Sudan from raising capital on U.S. financial markets. 1 The U.S. administration fiercely opposed Congress's proposal, fearing that such sanctions would ultimately hurt America.They would in any case have been largely symbolic; amid the devastation brought by the conflict, only three companies-from Canada, China, and Sweden-were still operating in Khartoum.Yet barring foreign businesses from tapping U.S. financial markets appeared to run counter to Washington's long-held commitment to the free movement of capital, one of the ingredients of America's economic success.Skeptics pointed out that to escape American sanctions, multinationals could also be tempted to raise debt or issue stocks in other financial centers such as
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.435 | 0.251 |
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".