State capacity and the unplanned decline of Venezuela’s petro-state: reflections for sustainable transitions and the Green New Deal
Bibliographic record
Abstract
Venezuela has historically been one of the world’s largest oil producers and has the largest crude reserves. However, the country has experienced a dramatic political and economic crisis over the past decade that has decimated its oil industry. Venezuela’s production shrunk sharply in the past six years, oil exports have declined and the country is now a marginal producer in global markets. This crisis has taken place amid a process of autocratic political consolidation and the establishment of a predatory political economy. This article focuses on this crisis and interrogates to what extent it can pave the way to a sustainable move away from oil dependence in dialogue with recent debates on sustainable transition processes. Building on the intersections of Global Political Economy and environmental politics, we highlight the importance of interconnecting links across state, society and international actors in viable sustainability transitions, such as proposals for Green New Deal(s) in different national contexts. Our analysis of the Venezuelan case subsequently highlights the absence of these capacities. We argue that contemporary Venezuela underscores the risks and costs of post-oil energy transitions in rentier states. Contemporary Venezuela is thus a cautionary tale for resource-dependent economies that may also undergo post-oil transitions in the future due to shifting global conditions but likewise lack the necessary state capacity to respond and adapt.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".