Myths and Giants: The influence of the European Union and the United States
Bibliographic record
Abstract
Tales of globalization invariably return to one of two geographies – the European Union or the United States. In the case of Europe this turning is a romantic longing: a sense that Europe is the peaceful way of the global future. With good luck and good governance we can imagine Europe is what globalization will mean for all of us: eroding borders and diminished sovereignty, all to mutual benefit. Europe is set on a course of steady expansion, spreading the blanket of strong human rights protections and robust international law ever eastward and southward. Candidate countries are eager to comply and join; enticed by the prospect of economic union, they rush to improve conditions of human flourishing. What could be better for all than to turn the globe itself into an area of freedom security and justice? The United States is the twin avatar of globalization. Although the image is not exclusively rosy, the United States is the sole remaining superpower, winner of the Cold War, uncontested hegemon. In contrast to Europe, the United States is engaged in a more “direct delivery” method for the global spread of democracy and human rights. There is enormous contestation within and outside the United States about the virtues and values of its war on terror. This is a debate about methods, not about the ultimately unambiguous good of conquering terrorism and bringing the disputes it represents within an appropriately broad democratic umbrella.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".