Developing Expert Scenarios Facing Iran’s Petroleum Industry
Bibliographic record
Abstract
This research discusses future development of Iran’s petroleum industry by using strategic management approaches relying on scenario-based planning models. The theoretical frame of this research is a normative paradigm in upper range documents advocate approach. Delphi methods, cross-impact analysis, and scenario-based planning have offered flexible and comprehensive planning combinations in proposing new styles in foresighting products development. In addition, Micmac software was employed to analyze dates. In this research, 235 influencing factors on a product development trend were selected using a PESTEL model and a Delphi approach, then the effects of these factors on each other were tested that eventually 22 key factors were selected. Among 22 key factors, 2 main factors including “political relations” and “the government’s dependence on petroleum” were selected using a cross-impact analysis. After that, a 2×2 matrix was formed that contains four scenarios including a playful rabbit, a runaway snake, a noble horse, and a sleeping lion. This research can enhance decision making abilities of top managers through identifying key signals of how each scenario appear in future of Iran’s petroleum industry. Results show that management team of petroleum products requires serious etiology and attitude rehabilitation. Key words : Futurology; Scenario planning; Iran’s petroleum products; Key factors; PESTEL model; Micmac software
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".