Bibliographic record
Abstract
Increasingly, the US State Department is relying on efforts of public diplomacy to improve America's image abroad. We test the theoretical efficacy of these efforts through an experiment. Participants were recruited in Kyrgyzstan and Tajikistan. All but those participants randomly assigned to a control group read a quote about the US. We varied attribution of this quote to President Bush, an Ambassador, an ordinary American or to no one. We then asked respondents a battery of questions about their opinions of the US before and after a long discussion with other participants about the US. We find that the identity of the messenger matters, as those who read the quote attributed to Bush tended to have lower opinions of the US. After the discussion, these views partially dissipated. Post-discussion views were more heavily influenced by how other participants viewed the US. After controlling for the source and location of the discussion, when the discussion took place among people with more positive initial views of the US, views of the US improved. However, when there was a large range of views in the discussion, post-discussion views of the US were relatively worse. Based on this study, we suggest new directions for the conduct of public diplomacy.
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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 | 0.007 |
| 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 | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".