The Temporality of Identity in Planned Cities: A Case Study of Zhong Xing New Village, Taiwan
Bibliographic record
Abstract
In 1957, the Kuomintang (KMT), Chiang Kai-Shek’s nationalist government, planned and built Zhong Xing New Village (ZXNV), a garden city, to house the Taiwan Provincial Government. Despite the benefits of public housing, healthcare, and education, ZXNV experienced a two-third drop in population after 1985. The political liberalization and democratization of Taiwan in the 1980s and 1990s led to the reclamation of Taiwanese national identity that rejected the hegemony of the KMT and the physical manifestations of this colonial history, including ZXNV. ZXNV was a utopian ideal constructed during a time of authoritarian rule for a specific political purpose and homogenous population. ZXNV’s inability to change its purpose and identity led to its ultimate depopulation. Ethnographic fieldwork reveals the changes in ZXNV’s built environment and neighborhood culture influenced by socio-political transformations over the last sixty years. Fourteen interviews were conducted with two generations of ZXNV residents, and archival research reveals the intended design and policies of the city. Key findings include the structural flaws in the city’s design, the exposure of political tensions between the national and provincial governments, and the changing national identity of Taiwan due to globalization, all of which led to the ultimate downfall of Zhong Xing New Village.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.003 | 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".