How does the ‘Belt and Road Initiative’ change urbanisation patterns in Southeast Asia?
Bibliographic record
Abstract
This paper examines how Chinese transnational investments, as (re)framed in the Belt and Road Initiative (BRI), contribute to changes in urbanisation processes in Southeast Asia. On the ground, the BRI becomes contextualised and intersects with local and national development trajectories. The growing presence of Chinese actors in the region intensifies urban dynamics, especially in secondary cities and emerging urban sites, where the BRI is used as a lever for local internationalisation strategies. The heterogeneous nature of the links between the BRI and various large urban projects is demonstrated on the basis of case studies involving changing consortia of private and public Chinese and Southeast Asian actors. A regional approach allows us to identify connections and shared processes across Southeast Asian countries. It provides a historically grounded understanding of how the BRI incorporates long‐term interactions with China and more recent partnerships in Southeast Asian countries. The paper paves the way for a research agenda that contests the image of China as a monolithic actor implementing the BRI uniformly and consistently. Further analyses are needed to examine systems and networks of actors as well as the local urban politics that affect the BRI on the ground.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".