Bibliographic record
Abstract
On 26October 2010, by a vote of 187 for, two against, and three abstentions, the United Nations (UN) General Assembly endorsed a Cuban-sponsored resolution reproving the United States’ unilateral illegal economic sanctions against the island and demanding its end.1 The two votes opposing came from the American and Israeli delegations. Washington’s most important allies, including the European Union (EU), Canada, Japan, and Australia, joined Cuba. It was the nineteenth consecutive time that the General Assembly had taken such an unbalanced pronouncement. How is it possible that a small state, lacking in the necessary attributes of ‘hard power’, is able to produce such a diplomatically successful outcome against the world’s largest superpower? One answer is that even Great Powers make mistakes and that the sanctions against Cuba, adopted during the Cold War, are a case in point. But it does not reveal the whole truth. Even if Washington’s Cuba policy is one of the most absolutely unilateral actions taken by its policy-making elite, it does not reasonably explain how Cuban diplomacy has garnered the support of practically the whole world, including countries that, in the last analysis, coincide with the United States in their ‘regime change’ policy towards Cuba.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".