A conceptual framework for the oil market dynamics: A systems approach
Bibliographic record
Abstract
The recent decade witnessed both steep ascending and descending trends in global oil prices. As a main energy resource, oil has been playing a substantial role in contemporary world’s economics. Hence, analyzing and understanding short- to long-term dynamics of oil prices is still one of challenging issues in the global economic- and energy-related debate topics. Although the literature is full of significant analytical studies that focus on particular issues related to the real oil prices, less can be found that integrates various factors affecting its dynamics. An integrated model, in which focal variables are included in main building blocks are illustrated with their interrelationships, helps policy makers to better understand the system. In this article, using a systems approach, a conceptual framework is developed to demonstrate various (economic and financial, technological, political, demographic, and industrial) factors that impact on the dynamics of the futures and spot prices with their interrelations. It is shown that unilateral (and univariate) analyzes is not sufficient in oil market dynamics analysis and systems approach should be applied. To do so, a subsystems diagram is developed based on literature review and analysis of oil market statistics. To validate the framework, windowed correlation analysis, Granger causality test, and regression analysis are utilized. Accordingly, a causal loop diagram is developed to describe interrelationships between main variables making the dynamics of oil prices. The framework provides practitioners with a foundation to conduct different related analyses. A quantified system dynamics model can be built based on this framework to simulate the market behavior and predict probable future trends and fluctuations.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".