Main Building as a Central Courtyard Pattern in Lasem, Central Java, Indonesia
Bibliographic record
Abstract
Lasem, a city of a thousand gates often referred to as "Petit Chinois" by foreign tourists, possesses extraordinary historical heritage as the former capital, making it a Heritage City.This is one of the reasons for choosing Lasem as a research study location.This article contributes by identifying typical spatial patterns in Lasem.In this study, the focus of observation is Chinese settlements located in Babagan Village, Soditan Village, Karangturi Village, and Gedongmulyo Village, where these areas are the locations for the development of Chinese settlements in Lasem.This paper aims to understand the development of the typology of Chinese residential courtyards in Lasem and their survival to the present day, so that they can become part of the national cultural heritage.The research method used is descriptive qualitative, involving literature studies, field surveys, and analysis using graph access.The results found that the typology of the morphological floor plans of the Chinese settlement houses in Lasem differs in accessgraphy from their country of origin.Courtyards, which serve as building shafts in their home country, are not found in the houses of the Lasem Chinese settlements.The courtyards in the Chinese settlements of Lasem are precisely positioned around the main building.The uniqueness of the typology of the Chinese settlement courtyards in Lasem is the result of the space occupied by humans to survive and the community's attachment to their place of residence.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".