Social Capital and Residential Agency in Pujiang New Town, Shanghai
Bibliographic record
Abstract
Although it barely registered in social media and current news in North America, the Shanghai 2010 World Expo was the most expensive urban reconstruction project in Chinese history and also caused the largest human relocation project in Shanghai history. To make way for the Expo, over18 000 families- an estimated 55 000 people- were relocated to the outskirts of Shanghai, away from their homes, communities, social connections and basic services. Of these residents, 25 000 were relocated to Pujiang new town: a brand new town constructed for this occasion by the Shanghai government. Although the government and contracted urban planners built the town, it is the relocated residents who are building the community. Using personal interviews that I and my Shanghainese partner conducted with the residents of Pujiang new town, we aimed to find out how residents are regaining the “social capital” that was lost during their forced relocation, and how their “individual and collective agency” prevents them from being seen as victims of a strong centralized government. In order to understand how this unique case of urban development was created, I will also be explaining the historical causes of the project, and it’s social and political consequences. However, it is the overarching question of “how does China see urban development, and why?” that I wish to answer
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".