Praktik Paradiplomasi dalam Implementasi Kerjasama Smart City Pemerintah Kota Bandung dan Kota Seoul
Bibliographic record
Abstract
Paradiplomacy was popular in the early 1980s, when the Quebec City government strengthened cooperation with regional governments of other countries and other state actors in international relations. This phenomenon was studied in depth by diplomacy experts, namely Duchacek and Soldatos, which was later implemented in practice in transnational relations between countries in the world. The same thing was done by the city government of Bandung. The Bandung City Government undergoes the stages of smart collaboration formulation. An important process in paradiplomacy is the occurrence of communication contained in the policy advocacy process of the Seoul City government through the Knowledge Sharing Program (KSP) under the Ministry of Economy and Finance of South Korea. This study aims to see the Bandung City government as a subnational government entity conducting diplomacy outside the context of traditional diplomacy, namely paradiplomacy in implementing Smart City cooperation with the City of Seoul in 2016-2019. This research was conducted using a qualitative approach with literature study methods. The literature study method is useful for gathering secondary information needed to support findings in research. This study produces a map of cooperation between the City of Seoul and the City of Bandung which has not been discussed in a similar study using a paradiplomation framework that combines the concepts of Duchacek, Soldatos and Keohane. The cooperation map referred to is an in-depth explanation of the smart city of Bandung which includes Smart Branding, Smart Living, Smart Environment and Smart Government.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".