[455] Internal Versus External Sources of Anti-Americanism TWO COMPARATIVE STUDIES
Bibliographic record
Abstract
To assess the relative importance of internal and external sources of anti-Americanism, both cross-sectional and longitudinal data were examined. Multiple regression analysis of the cross-sectional data—which cover 68 countries for the period 1956—1965—revealed that both internal stress and U.S. penetration affect anti-Americanism independently, with internal stress being a stronger determinant of anti-Americanism than U.S. penetration. When the countries were grouped together in regional clusters, stress was more important in generating European and Latin American anti-Americanism, while, in the Afro-Asian nations, U.S. presence was more important. Longitudinal data were collected and analyzed for Canada and Mexico, each for the period 1936-1968. Both simultaneous and time-lagged multiple regressions again showed stress and penetration as independent determinants of anti-Americanism. While, in Canada, U.S. presence was a stronger determinant of anti-Americanism than was stress, stress was more influential in generating Mexican anti-U.S. sentiments. In fact, in the Mexican case, U.S. presence was negatively related to anti-Americanism. Hostility toward the United States and its citizens is at least as old as Charles Dickens ’ portrayal of Americans in Martin Chuzzlewit (1843) as a collection of braggarts, buffoons, and charlatans. His nineteenth-century distaste for North American manners and mores has been largely superseded by less genteel twentieth-century opposition to Yankee economic expansion and Washington’s political activism in the inter-AUTHORS ’ NOTE: This article draws upon research papers prepared by two of
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".