Defining indicators for evaluating the residential environment in historic districts based on human needs: focusing on two cases in Northern China
Bibliographic record
Abstract
Compared to traditional communities, the residential environment in historic districts (HDs) is generally poor. Tourism development within HDs has affected these environments. As tailored assessment indicators are absent in HDs, this study introduces the historic district residential environment assessment indicator (HD-REAI) – a framework designed for the urban setting of HDs. The HD-REAI integrates Maslow’s theory and addresses the challenges and attributes of HDs. HD-REAI focuses on factors like housing property rights and district culture, which are pivotal for HDs. This enables a more nuanced and relevant evaluation of the residential environment in these areas. This study details the development of the HD-REAI and validates its efficacy through its application in two Northern Chinese HDs. The results demonstrate that the HD-REAI effectively assesses the environment, offering a specialized and context-sensitive tool. Moreover, different socioeconomic attributes have different effects on the assessment results. This study could provide a basis for constructing more refined and context-specific assessment tools to enhance residential environments in HDs
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".