Trumping foreign policy: public diplomacy, framing, and public opinion among middle power publics
Bibliographic record
Abstract
Even as the world’s sole superpower, the United States requires the cooperation of other states to achieve many of its foreign policy objectives. The President of the United States thus often serves as ‘Diplomat in Chief’ in public diplomacy efforts to appeal directly to publics abroad. Given Donald Trump’s antagonistic approach to foreign relations and widespread lack of popularity, what are the implications for support for US policy among publics abroad – particularly among middle power states allied to the US? While previous research on public opinion relying on observational data has found that confidence in the US President is linked to support for American foreign policy goals, the mechanisms at work remain unclear. Using original data from survey-based experiments conducted in Canada and Australia, this article seeks to clarify the effect of ‘presidential framing’ (presenting a policy goal as endorsed or not endorsed by Trump) on attitudes toward key policy issues in the Canada–US and Australia–US relationships. Results point to a negative ‘Trump framing’ effect in Canadians’ and Australians’ trade policy attitudes, but such an effect is not observed in other policy domains (energy policy in Canada, and refugee policy in Australia).
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 | 0.010 | 0.023 |
| 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 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".