MétaCan
Menu
Back to cohort
Record W4399002789 · doi:10.7910/dvn/r20tn3

Framing, Public Diplomacy, and Anti-Americanism in Central Asia

2010· dataset· en· W4399002789 on OpenAlexaff
Edward Levine Schatz

Bibliographic record

VenueHarvard Dataverse · 2010
Typedataset
Languageen
FieldSocial Sciences
TopicCentral Asia Education and Culture
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFraming (construction)Anti-AmericanismPublic diplomacyPolitical scienceDiplomacyCentral asiaPolitical economyMedia studiesHistorySociologyAncient historyLawArchaeologyPolitics

Abstract

fetched live from OpenAlex

The US State Department increasingly relies 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 religious tolerance and pluralism in the United States. We varied the attribution of this quote to President Bush, to an unnamed US Ambassador, to an ordinary American, or to no one. We then asked respondents a battery of questions about their opinions of the United States before and after a long discussion with other participants about the United States. 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 United States. After the discussion, these views partially dissipated. Post-discussion views were more heavily influenced by how other participants viewed the United States. After controlling for the source and location of the discussion, when the discussion took place among people with more positive initial views of the United States, views of the United States improved. However, when there was a large range of views in the discussion, post-discussion views of the United States were relatively worse. Based on this study, we suggest new directions for the conduct of public diplomacy.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.024
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0300.006

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.

Opus teacher head0.016
GPT teacher head0.289
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueHarvard DataverseSame topicCentral Asia Education and CultureFrench-language works237,207