Developing Social Resilience and Building a Culture of Nationalism in the City of Batam, Indonesia
Bibliographic record
Abstract
International ports serve and expedited interaction between nations. Building a modern international port city and advanced society dwellers within it heavily depends on socially sustainable development, and on the level of social resilience of its residents. The rapid development of an international port city cannot disregard many foreign interests in the City’s decision making; therefore, the lack of a culture of nationalism is investigated. This paper tries to portray that building social resilience is hand-in-hand with building a culture of nationalism, and it exists in the international port city setting such as the City of Batam. A mixed method analysis is used to get determinants of social resilience and nationalism. It is used a systematic review of peer-reviewed academic journal articles published between 2013 and 2018 to scope and synthesize assessment criteria; then it is compared with the quality of socio-cultural life condition from the survey and in-depth interview. The analysis results show a correlation between economic and political powers and building the local identity and culture of nationalism. In the context of being local, being national, and being "other" in the regional area, this study also shows that building a culture of nationalism is related to socially sustainable development, and nationalism is not correlated with the place of living but to the efforts of citizen participation in sustainable development. Therefore, building social resilience is also building a culture of nationalism, and it makes an international port city distinctly unique despite its internationalism characteristic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".