Bibliographic record
Abstract
hanghai has been a city in rising since the early 1990s and the sweeping changes in the urban landscape are most visible. In the early 1980s, the twenty-four story Park Hotel that had towered over Shanghai for half a century remained the highest building in the city and there were only four other buildings in the entire city that reached the height of twenty stories. By 1999, Shanghai had 1,350 buildings that were higher than twenty stories, nearly all of which were built in the 1990s.' The reshaping of the landscape of Shanghai was such that in the late 1990s, on average every three months, the authorities had to print a new version of the city map.2 The rise of Shanghai has been quite remarkably stimulated by the revival of the city's cultural tradition, which was established in the late nineteenth and early twentieth centuries but halted in 1949. Beijing's endorsement of Shanghai's growth has permitted more objective views on old Shanghai and its culture. Consequently, it allowed the resurgence of at least part of the city's old commercial spirit and cosmopolitanism. Under this circumstance, nostalgia about pre-1949 Shanghai was tolerated and to some extent even encouraged and promoted. This article looks into the nostalgia in presentday Shanghai, including an emerging coffee culture that symbolizes the
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".