On the New Town’s Rise and Fall and Location Choice Based on Alonso Model: A Case Study of Pukou University Town of Nanjing
Bibliographic record
Abstract
In the process of rapid urbanization, in order to adapt to the continuous development of the city, improve the city’s industrial structure and optimize the layout of urban space, many cities start the planning and construction of a new town. However, not all the new towns can develop well. Why? Based on the comprehensive literature review at home and abroad, this paper takes Pukou University Town of Nanjing as an example and uses the Alonso model to deduct the decision maker’s bidding curve, and tries to illustrate the reasons of the development advantages and disadvantages of new town from the perspective of new town’s location choice, thus it concludes: (a) in the same utility, the price that resident is willing to offer decreases with the increase of commuting distance (or time); (b) the increase of commuting distance (or time) will result in the decrease of land price (house price), and when the saved expenses can’t cover the increased commuting costs, the location’s prospect is worrying.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".