UPAYA STRATEGIS MENGGAIRAHKAN PASAR TRADISIONAL SEBAGAI PUSAT PEREKONOMIAN DI KOTA MAGELANG
Bibliographic record
Abstract
Traditional markets in Magelang City face significant challenges from competition with modern markets and the emergence of online shopping, which affect the local economy and community welfare. These markets serve as important centers for economic activity, job creation, price stabilization of essential goods, and support for small and medium enterprises (SMEs). The decline in market activity due to modernization threatens SMEs and access to goods at affordable prices. Government efforts to promote traditional markets remain suboptimal, mainly due to limited involvement and support. This study uses a policy evaluation approach with Grid Analysis and the preparation of policy recommendations with Interpretative Structural Modeling. The main findings of this research reveal that collaboration between the trade and tourism sectors, as well as the use of digital technology to promote traditional markets, are crucial steps to strengthen the existence of traditional markets in Magelang City. This study recommends increasing government efforts to promote traditional markets through collaboration with the tourism sector. These efforts are carried out by integrating sectoral programs between regional government institutions, in order to position traditional markets as the main tourist destination, thereby revealing the number of visitors and various other positive impacts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".