Bibliographic record
Abstract
The study of regions has been undergoing an intellectual `renaissance', resulting in a growing literature on the renewed importance and dynamics of varied forms of regions and regionalism (see Amin, 1999; Lovering, 1999; MacLeod, 2001). However, insufficient research has been devoted to the `crucial middle' role of regions in bridging and integrating global, national, and local economies. This role also turns regions into highly contested terrains for the diverse tensions and outcomes of economic integration, or lack of it, to play out. These include simultaneous tendencies in competitive and cooperative policies and practices of subnational and local governments versus those of global and local firms, as well as shifting opportunities and constraints on economic development and industrial upgrading. In this article, I advance a thesis that new regional dynamics are capable of mediating or restructuring global-local economic relations in varied ways to either facilitate or hinder the course of local economic growth and industrial upgrading. This thesis is elaborated and validated through a comparative analysis of the Pearl River Delta (PRD) and the Yangtze River Delta (YRD) in China — two of the most dynamic manufacturing regions in the world. This analysis focuses on how the organizational and spatial formations of regionalized global-local production networks, the regional urban hierarchy, fierce inter-local competition, and decentralized governance have led to rapid economic development in two historically and geographically well-endowed regions but may impede industrial upgrading that is crucial to sustaining their economic development. The article concludes by offering improved regional governance approaches as collective solutions to what appears to be a spatially fragmented microeconomic challenge of upgrading to sustainable economic development.
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 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".