Comparing Transport Corridors Based on Total Economic Cost
Bibliographic record
Abstract
This paper compares the performance of three competing corridors serving landlocked SADC countries (Beira, Dar es Salaam, and Durban) based on total economic cost from the perspective of transporters, retailers, and manufacturers. The motivation for the research is the paradox that, while Beira is closest to the hinterland served by these corridors, it attracts the least cargo. Historical research compares corridors in terms of both direct costs and time delays, but without translating time delays and variability in time delays into the economic costs experienced by corridor users. Unpredictable time delays reduce the competitiveness of cargo owners forming part of global just-in-time value chains. Our novel TEC model includes direct costs and the cost impact of delays and variability in delays and quantifies the relative contributions of ports, border posts, and road travel. The Port’s efficiency proved to be the biggest differentiator between these corridors, followed by border posts and road links. We found that while the Beira corridor has the lowest cost if only average travel time is considered, the Durban corridor proves to be the most competitive when variability in time delays is also considered, explaining why Durban enjoys the largest share of cargo transported to the landlocked hinterland.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".