Bibliographic record
Abstract
Purpose Oil exporters have been identified as having problems in the friendliness and performance of their logistics sector, particularly in general cargo group. The purpose of this research is to identify, through proposed data envelopment analysis (DEA) models, those oil exporters with most potential to develop (who have resources for this through their economic prosperity). Benchmarks of improvement are not only identified among top oil exporters, but also from larger group of emerging countries. Design/methodology/approach The research uses two different DEA models, and quantitative data available from economic prosperity and general cargo logistics performance. Models are input oriented, and use the most recent data from year 2009. Findings Major oil exporters are not homogeneous group in their performance turning logistics competence on prosperity. Actually, there could be one group of very low performance identified in general cargo logistics performance as compared to the DEA models, and other group with similar or slightly above performance with general cargo handling. Those performing at the lower end, and with most development potential, include such countries as Russia, Venezuela, Algeria, Qatar, Azerbaijan and Turkmenistan (possibly also including Iran, Kuwait and UAE). From the group of major oil exporters, these countries should learn exceptional logistics competence from Malaysia and Canada. Similarly, this research shows that the emerging economies, particularly China, but also India as well as Philippines, Thailand and South Africa are useful benchmarks to develop general cargo logistics performance further (to some extent Korea (South), Taiwan, Czech Republic, Poland, Turkey and Lebanon also could be included). Research limitations/implications The research is not based on longitudinal data, and should be enlarged to take into account earlier logistics performance index ratings from the World Bank studies (year 2007); this not only to verify the results, but also to highlight possible progress in the development of the logistics sector. Also, other global logistics performance ratings (e.g. from infrastructure) should be taken into account, and DEA models be developed further (often infrastructure drives other layers of performance). Originality/value Research work is seminal to study in respect to identifying which countries “lower performing oil producers” include, and which have potential in better developing their logistics sector (general cargo). Among this, research also proposes benchmarks for these countries, not only among oil exporters, but also from other emerging economies. The research findings give a unique and fresh perspective on the logistics performance of oil exporters and emerging economies in general.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".