The Role of Economic Intelligence in Accelerating Welfare of Gorontalo Province
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Gorontalo Province region has the potential for significant resource wealth. However, the Gorontalo Provincial Government still faces problems and challenges when trying to improve the welfare of its people. One is the limited data/information that is accurate, up-to-date, and integrated. This data/information is important in planning and making appropriate decisions. Another problem is that the community's role in modern development based on economic intelligence in Gorontalo Province has not been optimal. This means that the public and even bureaucrats are still unfamiliar with "Economic Intelligence." As a result, economic intelligence has not been fully used in planning, implementing programs/activities, evaluating and monitoring, or government decision-making processes. This research aims to analyze the problems and challenges in modern development based on economic intelligence in Gorontalo Province. This research uses a qualitative-descriptive approach. This study found that the main problems in modern development based on economic intelligence in Gorontalo Province are: 1) The lack of integration of economic data/information from various government and private agencies; 2) Lack of human resources (HR) who are reliable and have competence as managers and/or analysts of scientific data; and 3) Lack of technological support in managing economic data/information. The results of data analysis also show that the role of society in modern development based on economic intelligence is still very low. A lack of public understanding of the importance of economic intelligence causes this. Therefore, it is recommended that the Gorontalo Provincial Government make: 1) efforts to integrate economic data/information from various government and private agencies; 2) increase human resource capacity in managing and analyzing scientific data; 3) provide technological support in managing economic data/information; and 4) increase public understanding of the importance of economic intelligence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it