Twiplomacy by Indonesian Ambassadors and Embassies
Bibliographic record
Abstract
Twitter has been strategically used by many countries in the world as part of their digital diplomacy or known as Twiplomacy. The current pandemic highlights the pivotal role that Twiplomacy has to offer. Aside from being free to use, this approach is fast in terms of disseminating information that can improve a country’s international image. It also serves as an excellent tool to ensure protection for diaspora communities worldwide by providing updates to those in need of support. The Indonesian government is fully aware of these functions, hence almost all embassies and ambassadors are now on Twitter to push forward Indonesia’s international agendas. Our paper introduces a dataset which consists of key information from all Twitter handles owned by ambassadors and embassies as of 12 March 2021. The descriptive analysis offers a novel empirical exploration of how Indonesian Twiplomacy fares during pandemic times. Our data suggests that embassies have a substantial role in Twiplomacy and every effort to improve their digital contribution should be highly encouraged. As for digital reach, we found that the total number of embassies’ Twitter followers is less than a quarter of those of ambassadors. Yet, Indonesian embassies tweeted three times more than the ambassadors. We also find that embassies with longer existence in the Twitterverse have more followers, hence greater digital contribution to Indonesia's overall Twiplomacy performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".