Relationships of Selected Key Logistics Factors and Logistics Performance Index of Sub-Saharan African Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Logistics and supply chain bottlenecks are magnified with inefficient business processes and can result in increases of trade costs. Logistics Performance Index is a measure of how well different countries perform in their logistics activities to increase trade efficiencies. This study tries to explore the relationship of critical logistics factors with logistics performance index (LPI) developed by the World Bank. By taking Rwanda as a case study, the paper also explores the performance differences in logistics between landlocked and coastal countries, among countries within the same region, and income group. It shades light how a landlocked and low-income country was able in a decade to improve its logistics performance. The findings of two-stage least square provides a single estimated logistics index. It can explain the multiple logistic indicators which can be used to improve the ability to compete and improve logistics performance. Moreover, countries in the study, as well as other countries can utilize this estimated index to target policy actions to improve logistics operations.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it