Container Port Selection in West Africa: A Multi-Criteria Decision Analysis
Bibliographic record
Abstract
The West Africa gross domestic product is expected to grow and port expansion projects will increase capacity by over 12 million TEUs (Twenty-Foot Equivalent Units) by 2020. With the economic potential that the region offers and the steady growth of container traffic, the port selection decision by shipping lines is complex because the region has a poor shipping infrastructure and political instability that impact transportation security supply chain services. This research applies a multi-attribute value theory (MAVT) with value-focused thinking (VFT) and alternative-focused thinking (AFT) methodologies to develop a shipping lines’ container port selection decision models for West Africa. Criteria and port alternatives from a previous published study were used in the research. The study demonstrates that a decision analysis model can be developed based on available quantitative port data rather than using data from surveys, interviews and questionnaires, as done in previous publications. In both approaches the Abidjan Port is the best option for shipping lines and the worst option is the Lagos Port. The VFT approach offers graphical displays that help decision makers understand strengths, weaknesses, tradeoffs, and improvement opportunities for each port alternative.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".