A TRANS-PACIFIC PLANNING EDUCATION IN REVERSE: REFLECTIONS OF AN AMERICAN WITH A CHINESE DOCTORATE IN URBAN PLANNING AND DESIGN
Bibliographic record
Abstract
In this chapter, I present my own experience as a graduate of the masters program in Architecture and City Planning at the Massachusetts Institute of Technology, who attended Tsinghua University in Beijing to became the first American to earn a Chinese doctorate in urban planning, and who has continued to remain engaged in Chinese urban planning from bases both in Canada and in the United States. The aspect of my experience that is “in reverse” is that the usual transnational educational path for planning students seeking higher degrees is from “South” to “North” (which includes Europe and Japan, as well as North America), or from “East” to “West.” Here, I try to apply my reversed perspective to the understanding of how and why ideas and practices of planning do or do not “travel,” and in particular to how a moment in history affects the reception of foreign ideas. More personally, I reflect on how my education at MIT prepared me (or how it didn’t) for further learning and action in China, and, inversely, how experiencing Chinese planning provided a frame for appreciating my American education.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".