The 2016 Election and America’s Standing Abroad: Quasi-Experimental Evidence of a Trump Effect
Bibliographic record
Abstract
Global favorability toward the United States declined by more than 10 percentage points from 2016 to 2017. This shift coincided with the end of the Obama administration and the inauguration of Donald Trump—but did Trump’s election cause America’s standing abroad to erode? Leveraging a natural experiment, we show that Trump’s victory had an immediate, negative effect on international public opinion toward the United States. Our identification strategy exploits the fact that a major cross-national survey, the AmericasBarometer, was in the field when the 2016 US presidential election occurred. Using data from four Latin American countries, we compare respondents surveyed just before and after the election. We find that Trump’s unexpected win caused a sharp drop in trust in the US government. While scholars have long observed that domestic political considerations shape leaders’ foreign policy decisions, we show that domestic political events—such as elections—can also affect a country’s international image.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".