Chinese impact on development in Venezuela: the dynamics of structural stagnation
Bibliographic record
Abstract
This article traces the impact of China’s engagement on development in Venezuela from 2001 to 2023. We build on long-established theories of development and evaluate how the relationship has influenced three conditions crucial for development: industrialisation, economic diversification, and institutional strength. We discuss the impact of Chinese engagement in three phases: the first phase runs from 2006 to 2016, when bilateral investment funds were active; the second phase, from 2016 to 2023, is characterised by the sanctions imposed by the U.S. and Venezuela’s economic collapse. The third phase begins in 2023, marked by an attempt to reactivate the Venezuelan private sector through Special Economic Zones (SEZs). We argue that while China has emphasised its engagement in the Global South as one that prioritises development, in Venezuela, it has, in fact, contributed to the opposite. Earlier in the relationship, oil dependency deepened, and the governance of the binational development funds lacked accountability and oversight, producing feedback loops that countered diversification and weakened institutions. In recent times, the relationship has turned into a more traditional dynamic of dependency: Venezuela receives limited investments for low-productivity jobs and serves as a market for consumer products while exporting commodities, resulting in a structural stagnation.
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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".