Chinese Business Networks and the Globalization of Property Markets in the Pacific Rim
Bibliographic record
Abstract
In analyzing the extension of Chinese firms across space, one of the key industries to lend insight to this process is unquestionably the real-estate industry. While much of the vast ‘overseas Chinese’ literature has focused on the articulation of production networks, sub-contracting connections, and flexible accumulation and credit practices, relatively little attention has been paid to the property development process. This is a serious omission, for two reasons. First, it has become increasingly apparent that Chinese firms are currently among the foremost players involved in the development and acquisition of both commercial and residential projects worldwide; this includes mammoth urban development schemes in cities as diverse as Singapore, Vancouver, Sydney and Shanghai (Mitchell, 1995; Olds, 1995, 1998), and smaller projects throughout East and Southeast Asia (Yoshihara, 1988; Smart, 1997). Second, recent globalizing trends that have been studied in the areas of production, finance, migration, information and culture have also had a great impact on the property development industry, yet have not received the same degree of scholarly attention (Beauregard and Haila, 1997; Haila, 1997; Olds, 1995). The contemporary globalization of property markets affects key cities around the world, and is changing both the form and process of the development and marketing of real estate. By extension, it is also changing the overall shape of the cities, and engendering urban social change (and social conflict) (see Mitchell, 1993a; Ley, 1995). Despite this, however, little research has been devoted to the factors facilitating and shaping the globalization of property markets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".