قدرت پیشرانی بخش نفت و گاز در اقتصاد ملی و منطقهای (مطالعه موردی ایران و کانادا)
Bibliographic record
Abstract
In oil-exporting countries, it is important to have a clear evaluation of the oil sector at the national and regional levels. In input-output literature, the traditional and extraction methods are often used to analyze the status of economic sectors. These methods have two major shortcomings: double-counting of linkages and having a flaw to show the changes in income of the labor. In this paper, to overcome these shortcomings and to provide a more realistic picture of the status of the oil sector at national and regional levels, a comparative comparison has been used between Iran and Canada focusing on their two major oil-exporting provinces, Khuzestan and Alberta. For this purpose, the production-to-production approach based on the Sraffa-Pasinetti-Leontief theoretical model which its main concept is the induced effect of value-added will be used. The results show that the oil sector creates 0.0435 and 0.0372 units of induced value-added in Iran and Khuzestan. In Canada and Alberta the corresponding figures are 0.3173 and 0.4382. Therefore, this sector has more interdependency with the other sectors in both national and regional levels in Canada (as a well-developed country) than Iran (as a developing country). However, services and industry sectors absorbed more decomposed induced value-added of the oil sector in comparison to other sectors. Therefore, national and regional policies should be implemented to have diversified products and prepare the requirement of having the most of interdependency prerequisites between the sectors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.021 |
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".