Strategic-Competitiveness for Socio-Economic Development: Unlocking the Potential of Gorontalo Province in the Context of the Nusantara Capital Integrated Development (IKN)
Bibliographic record
Abstract
The development of the Capital City of Nusantara (IKN) in East Kalimantan Province was an important turning point in Indonesia's development model. As one of its close neighbors, Gorontalo Province has great potential to contribute to developing IKN. Cohesion between Gorontalo Province and IKN is key to achieving its socio-economic development goals. However, Gorontalo Province still faces problems in economic development, public services, and MSMEs, including slow local economic development, inadequate infrastructure, low community competitiveness, high levels of poverty, and socio-economic problems. This study aims to analyze the problems and potential of the Gorontalo Provincial Government in its contribution and cohesion to the socio-economic development of IKN. Through a well-being methodology (WM) approach combining qualitative and quantitative approaches, three main dimensions are emphasized: perception, participation and community acceptability of competitive strategies to provide a holistic and participatory understanding of welfare aspects. The study found that the people of Gorontalo have a positive view of IKN, with the hope that the Gorontalo Provincial Government can strengthen its contribution through improving the quality of human resources, infrastructure and economic competitiveness to support the socio-economic development of IKN. Interestingly, most respondents were generation Z. This shows that most of Gorontalo's young generation were involved in this research. In essence, the Gorontalo Provincial Government faces challenges in its contribution and cohesion to the socio-economic development of IKN, with low levels of public participation and a lack of public understanding of the benefits of development. Therefore, intensive efforts are needed in socialization, education, and public involvement to increase participation and strengthen the IKN development process. The Gorontalo Provincial Government is advised to design an integrated competitive strategy, focusing on increasing public perception through socialization, building trust, fostering public participation, and increasing the acceptability of IKN 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 | 0.001 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".