Evaluation Framework and Model of Oil Industry's International Competitiveness
Bibliographic record
Abstract
In the occasion of economic globalization, competitiveness of the pillar industry has become the core of the regional competitiveness. As the largest industry in the world, oil industry’s international competitiveness is referred to as the important figure of one country’s comprehensive competitiveness. So it may discovery a large of information for people by structuring an evaluation framework of oil industry. We need create a set of evaluation index system when structuring an evaluation framework, so it means that the first step for us to do is to find suitable evaluation indexes. In this paper authors created an evaluation index system of oil industry to evaluate its international competitiveness, which was structured from the aspects of current competitiveness, potential competitiveness and environmental factors. Then a fuzzy evaluation model based on two-base-point method was designed to act as the evaluation model. And we can evaluate oil industry’s international competitiveness of any country by the model. An empirical analysis was made by several selected well-known oil-producing countries, and it showed a good result of evaluation. Key words : Well-known Oil-producing Countries, Industry competitiveness, AHP, Evaluation Framework and Model
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".