Do Foreigners Count? Internationalization of Presidential Campaigns
Bibliographic record
Abstract
The U.S. presidential elections always attract the attention of foreign audiences—who, despite not being able to vote, choose to follow the campaigns closely. For a post that is colloquially dubbed as the “Leader of the Free World,” it is not unexpected to see such an interest coming from nonvoters. Mimicking almost hosting a megaevent, the elections increase the media coverage on the United States, thus making the elections a platform to communicate with the rest of the world and to influence the reputation of the country, or its nation brand. This study postulates that the increasing adoption of social media by campaigns as well as ordinary users, increase the symbolic importance of presidential elections for foreign audiences in two ways. First, foreign audiences no longer passively follow the campaign but rather present their input to sway the American public opinion through social media campaigns. Second, foreign audiences are exposed to a variety of messages ranging from official campaigns to late-night comedy shows to local grassroots movements. The audiences both enjoy a more in-depth understanding of the elections campaigns and are exposed to alternative political views. In this study, the 2016 U.S. presidential elections are positioned as a megaevent that can influence the American nation brand. Through a comparative content and network analyses of messages disseminated over social media in the United Kingdom, Turkey, Canada, and Venezuela, the nation branding–related impacts of election campaigns are investigated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".