Economic Efficiency of the Main Oil Producing Countries in Upstream Sector in the Period 2010-2017
Bibliographic record
Abstract
This work aims to calculate the economic efficiency of the main upstream oil producing countries in 2010-2017, using the Data Envelopment Analysis (DEA) methodology. In the begining the technical efficiency is determined, next allocative efficiency is calculated to finally obtain the economic efficiency. The countries analyzed were: United States, Russia, Canada, China, United Arab Emirates, Kuwait, Brazil, Kazakhstan, Mexico, Angola, Venezuela, Algeria, United Kingdom, Holland, France and Spain. It can be seen from the results that no country was efficient in economic terms. However, Russia had the highest levels of economic efficiency, on the opposite side, France has the lowest values in this indicator. It is recommended to establish strategies in the sector to avoid economic vulnerability in some countries. The main limitation is the availability of the data. The originality of the research consists in obtaining economic efficiency in this industry, since there are no studies with these specific characteristics. It is concluded that in terms of economic efficiency, there was no adequate use of resources in the upstream oil industry.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".