TIPOLOGI INTERREGIONALISME UNI EROPA DALAM MERESPONS PENULARAN PANDEMI COVID-19 DI EROPA
Bibliographic record
Abstract
Globalization of the Covid-19 pandemic has caused a very rapid contagion effect between regions around the world and has created a multidimensional crisis in European Union countries, so an interregionalism approach is needed. This study aims to analyze the typology of European Union interregionalism in response to the contagion of Covid-19 pandemic in Europe. By using analytical descriptive methods combined with Mathew Doidge’s theory of interregionalism, Heiner Hänggi’s typological concepts of interregionalism, and data analysis techniques using Miles and Huberman’s models, this study found that European Union adheres to three types of interregionalism, namely group-to-group interregionalism, biregional-transregional interregionalism, and hybrid interregionalism. Group-to-group interregionalism is carried out by building dialogues and partnership cooperations between regions related to economic recovery and capacity building to strengthen the governance of Covid-19 pandemic with ASEAN, African Union, and Mercosur. Meanwhile, biregional-transregional interregionalism is carried out by building dialogues and partnership cooperations between regions related to capacity building to strengthen the governance of the Covid-19 pandemic with ASEM countries. Finally, hybrid interregionalism is carried out by building dialogues and bilateral partnership cooperations related to economic recovery and global health with the United States, China, Japan, Turkey, and Canada. European Union interregionalism in response to contagion effect of Covid-19 pandemic functions as a means of power balancing, collective identity formation, agenda setting, institution building, and rationalizing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".