The cooperation‐infrastructure nexus: Translating the ‘China Model’ into Laos
Bibliographic record
Abstract
The Belt and Road Initiative (BRI) is frequently described as a ‘hard’ infrastructure programme of roads, railways, and ports. But a variety of ‘soft’ development cooperation activities—including trainings, dialogues, research, and development projects—have also been established under the banner of the BRI. We examine how this ‘soft’ cooperation works hand‐in‐hand with ‘hard’ infrastructure in what we call the cooperation‐infrastructure nexus . This nexus works in two parallel ways: cooperation activities establish discursive frames of Chinese development as a model to follow, while also creating channels of exchange and support that validate and facilitate infrastructure investments. These investments, in turn, legitimize and often provide direct channels for further cooperation. To make this argument, we present empirical research conducted in China and Laos in two sectors: hydropower and rubber. Both expanded rapidly in China over the past three decades and are held up as models to follow; both are also the basis for cooperation and infrastructure interventions across Southeast Asia. We show that Chinese rubber and hydropower cooperation activities serve both to frame existing projects positively and facilitate future investments in those sectors in Laos. The infrastructure established on the ground, however, rarely resembles the ‘China model’ upon which it is discursively based. Instead, we find that important obstacles and contradictions arise in translating China's domestic achievements into other country contexts. Our findings show the need to consider cooperation as intertwined with infrastructure, while acknowledging the disconnect between discourses of a China model and experiences on the ground.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".