Why multinational development corridors don’t move ahead: insights from the Bioceanic Corridor in South America
Bibliographic record
Abstract
Development corridors have become a key tool of economic policy in the Global South. Yet, it appears that many of these mega-projects already fail at the stage of implementation. The article deals with three problems that corridors face. Corresponding ideas are drawn from existing literature, and confirmed and expanded against the backdrop of a case study on the Bioceanic Corridor, which connects the central west of Brazil via Paraguay and Argentina to the north of Chile. First, there is a tendency to focus on opportunities and neglect challenges. Corridors often reflect unrealistic grand visions for the future. Second, these initiatives depend on territorial rescaling to sub- and supranational levels, but national governments bundle too much power. Third, being a means to facilitate integration into global value chains, corridors provoke disputes over gains. Each country – and even subnational entities – strives to maximise its benefits at the expense of others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".