Contextual Sensitivity Analysis for Urban Industrial Heritage Quarter Regeneration: Shanghai as a Pilot Case Study
Bibliographic record
Abstract
Looking into the development of modern cities, industrial heritage quarters within the urban context are important spatial resources for urban development. With the increasing trend toward incorporating quantitative research in urban studies in recent years, this study aims to develop mixed method research named Contextual Sensitivity Analysis (CSA) to study how the urban context of industrial heritage quarters impacts the variation in heritage values after regeneration. The application of CSA proceeds as follows: first, value variations following urban regeneration are established as the analysis targets; then, the impact of adaptive reuse strategies on the context is quantified as analysis indicators. A mathematical model is employed to explore how these indicators influence changes in values. This study takes Shanghai as a pilot case study and selected 14 samples that accord with the characteristics of urban industrial heritage quarters (UIHQ) for data collection and analysis. The findings of the analysis will be displayed as regression curves, demonstrating that the degree of correlation and the impact trend between specific context indicators and heritage values vary significantly. By identifying and comparatively analysing indicators with stronger correlations, the study reveals which contextual factors are more effective and efficient in influencing particular heritage values under certain conditions. These results confirm the feasibility and usefulness of CSA as a method for uncovering the relationship between surface-level outcomes and underlying contextual causes in urban industrial heritage quarters. In conclusion, this study is expected to provide a reference when considering how the resource input should sensitively focus on different indicators to achieve optimal performance in adjusting the value of heritage sites. The potential of this study also lies in the fact that, if the CSA method proves effective, the value targets and contextual indicators can be further expanded and applied in broader future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".