EVALUASI KEPENTINGAN DAN KEPUASAN MASYARAKAT TERHADAP LAYANAN FREE TRADE AGREEMENT CENTER DI INDONESIA
Bibliographic record
Abstract
Indonesia already has several trade agreements with its partner countries, but their utilization is still not optimal. One of the causes is asymmetric information between the government and private sector firms, where the information obtained by these firms is not received perfectly. To that end, the Ministry of Trade established a Free Trade Agreement (FTA) Center to assist the government in overcoming asymmetric information and optimizing the use of trade agreements. To that end, the Ministry of Trade established the Free Trade Agreement Center or FTA Center to assist the government in overcoming asymmetric information and optimizing the utilization of trade agreements. This study measures the suitability of the implementation of the tasks and targets of the FTA Center in five regions by evaluating the interests and satisfaction of the firms towards the FTA Center's services by distributing questionnaires and interviews as data collection techniques. Based on the respondent's data, the results show that in the aggregate the results of the study show that various aspects of satisfaction and interest are already at a high satisfaction and importance index, namely openness/easy access to information, attitudes of experts, abilities and skills of experts, access to services, service effectiveness and efficiency, as well as complaint handling. However, the FTA Center can improve the quality in the aspects of service information, facilities/infrastructure, and service completion time
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".