Balanced Transport and Sustainable Urbanism: Enhancing Mobility and Accessibility through Institutional, Demand Management, and Land-Use Initiatives
Bibliographic record
Abstract
Chinese cities and other rapidly urbanizing parts of the developing world face immense challenges as they attempt to balance and jointly pursue economic development and environmental sustainability objectives. Chinese cities like Beijing, Shanghai, and Dalian are experiencing motorization rates of 20% to 25% annually.1 The sheer scale and rapidity of growth in the population of automobiles is daunting, challenging and at times overwhelming the institutional and administrative capacities of local and national agencies to build sufficient infrastructure and strategically plan for and manage travel demand.Over the past decade, many Chinese cities have adopted a traditional western approach in responding to mounting problems of traffic congestion, airborne pollutants, rising accident rates, and other ills associated with automobile-oriented societies. This has been one of mainly technological and supply-side solutions, in the form of super-freeways and viaducts, expansive roadway capacity, intelligent transportation systems (ITS), and other technical exigencies that seek to accommodate pressures wrought by rapid motorization.Might this be placing Chinese cities on the same trajectory followed by most American cities and increasing numbers of those in Europe, Canada, and Australasia: rising automobile dependency and the associated problems of galloping sprawl, fossil-fuel resource depletion, high rates of greenhouse gas emissions, and social exclusion by class and ethnicity?
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.016 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".