Evaluasi Pasar Tradisional: Studi Kasus Pasar Rakyat Krueng Mane
Bibliographic record
Abstract
The Krueng Mane People's Market is one of the traditional markets built by the government. Traditional markets are part of the economic facilities and infrastructure in Indonesia. However, its existence is increasingly being eroded by the modern market and the less than optimal use. The government has launched a Market Revitalization Program, but many have failed. The purpose of this study was to determine the use and design criteria of the functional aspects of standard elements of space, accessibility, zoning and facilities. This study uses an evaluation method with a qualitative descriptive elaboration. In collecting data, the method used is place centered mapping on field observations, surveys using questionnaires and interviews. The results of the study indicate that the aspects studied have not been effective for use by traders and buyers during their inhabited period. Therefore, it is suggested that the design criteria are the size of the trading room based on the standard, the width of the circulation does not vary in the sense that it must comply with the standard, the zoning should be given a clear marker and the facilities for the market must be provided again so that the market that has been built can be used optimally in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| 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 | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".