Collaborative Governance in Local Economic Development: the Case in East Java, Indonesia
Bibliographic record
Abstract
Purpose: The aims of this study is to analyze the local economic development in Sumenep Regency with a collaborative governance perspective. We employed qualitative research techniques that focused on describing and understanding the subject. Methods: Our analysis of the data involved interactive methods. We conducted in-depth interviews with 30 individuals who represented various stakeholders in local economic development, including the government, private sector, community, media, and universities. Research is focused on context systems, collaboration drivers, collaboration dynamics and collaborative action. In this study, the weak collaboration context system is caused by weak network ties and low stakeholder interaction frequency. Meanwhile the leadership factor has an important role in building and strengthening stakeholder networks and interactions as a driving force for collaboration. Finding: The findings of this study construct the theory of collaborative governance put forward by Emerson and Nabatchi (2015) by adding elements of social capital, forming collaborative institutions (Koperasi or BUMDes) and strengthening collaborative institutional capacities. Contribution/Originality: The research uncovers the significance of collaborative institution formation (Koperasi or BUMDes) and the role of social capital in successful collaborative governance for local economic development. These research suggests a collaborative governance model based on Emerson and Nabatchi's regime combined with collaborative institution formation. Emphasizes the importance of strengthening social capital and collaborative institutions for achieving successful collaborative goals in local economic development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".