Bibliographic record
Abstract
The author is President of Chreod Ltd., a consulting firm he founded in 1985 in Canada. Since 1988 the firm has worked on over 80 urban and regional development consulting projects in over 70 cities across China. Mr Leman's work has largely been on strategic development planning and policy development for municipal governments in China, and for the World Bank and Asian Development Bank. He has worked in Shanghai,Tianjin, Beijing, Chongqing, and in Anhui, Hebei, Henan, Gansu, Jiangsu, Zhejiang, Guangdong, Guangxi, Guizhou and Sichuan Provinces. Mr Leman has published articles on China urban issues in Ekistics, the Asian Wall Street Journal, the World Bank's Urban Age Journal, and the Far Eastern Economic Review's China Trade Report. Mr Leman is a member of the World Society for Ekistics and served as a member of its Executive Council from 1995-1998. This article is derived from his presentation at the international symposion on 'The Natural City," Toronto, 23-25 June, 2004, sponsored by the University of Toronto's Division of the Environment, Institute for Environmental Studies, and the World Society for Ekistics, and a subsequent paper that he delivered at the World Bank Urban Research Symposium held in April 2005 in Brasilia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".