How Local City and Its Hinterland Impact Global Firms: An Empirical Test of Beijing
Bibliographic record
Abstract
The location of global firms in a city has been regarded as a critical factor for a world city classification. Context factors of world city at different levels have also been recently considered to influence the location of global firms. In this paper, we use dynamic factors at three levels of the host city and its hinterlands namely state (country), city, and service sector to analyse the relations between these factors and the global firms during the globalization progression of Beijing. We use comprehensive data sets from Beijing Statistical Yearbooks from 1988 to 2014 for the city, firm level, and service sector factors; and the China Statistical Yearbook for the same period for the country level (Gross Domestic Product-GDP) factor. Using a Granger causal test, we find that the relationships among the four factors are not symmetric; especially, factors of larger scopes have more significant effects on the ones of smaller scopes than vice versa. This therefore shows that global firms tend to locate at the leader city with access to the big market of the national economy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".