Social Norms Framework of Suku Laut in Traditional Coastal Settlement of Mainland Batam
Bibliographic record
Abstract
The SDGs expect industrial development to contribute to human welfare, including traditional ethnic minorities.Since 2000, industrial development in Batam has led to urbanization and limited land, causing expansion of development to traditional coastal settlements and affecting changes in social norms of the community, which threaten the loss of social and cultural values.This problem needs to be addressed by the government efficiently and inclusively to ensure sustainable development that preserves and strengthens Batam's local identity.This study aims to formulate a social norm framework as material for government consideration in developing strategic policies in planning for the sustainability of the existence of Suku Laut in Batam.The research used a mixed method through a questionnaire among the Suku Laut community, field observation, and expert interviews.This study used Likert's Summated Rating, SPSS, Analytical Hierarchy Process, and City Resilience Framework theory.According to the findings, Suku Laut in mainland Batam has four social norms framework indicators defining its social norms framework, such as place (environment and infrastructure), organization (society), people (wellbeing), and leadership strategy (knowledge).In short, the government's consideration in sustainable planning and management is providing adequate education by empowering stakeholders and providing marine infrastructure in coastal settlement areas.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".