A Probe into Impediments of Benefit Gain in Investment Contracts of Oil and Gas Industries
Bibliographic record
Abstract
Generally, oil contracts comprise of a set of benefits in their own specific settings. Different technical, sometimes fundamental, factors may lead to imbalance in benefiting from the contract; this is considered as injustice in international relations and a violation to purpose of law. Therefore, seeking strategies to deploy justice in this field and offering strategies to find such strategies is of crucial importance. The present study is aimed at investigating impediments and factors that cause imbalance in equal gains of both parties in these contracts. Identification of these factors and the existing conditions significantly help realization of justice within a framework of rules and regulations. After carrying out deep analyses of oil contract and evaluation of the results, factors influencing this were identified; some of this factors such as contract parties and selection of the type of contract and currency system are related to the parties’ will sand some others are related to conditions such as risks of economic policy making and of economic structures, while there is another case of gas and oil price fluctuations which is separate from the existing conditions or parties’ wills. Therefore, it is of great importance to take these issues into account and to make attempts to follow them toward gaining benefit from oil contracts for oil companies, particularly those in countries where oil is the aim source of income.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".