Correlation Between Human Development Index and Economic Growth of Major Oil and Gas Producers
Bibliographic record
Abstract
This paper examines the correlation between the Human Development Index (HDI) and the economic growth of the top oil and gas-producing countries. The 14 largest producers selected for analysis are the USA, Russia, Saudi Arabia, Canada, Iraq, China, United Arab Emirates, Brazil, Kuwait, Iran, Qatar, Australia, Norway, and Algeria. These countries include both developed and developing countries. It is also important to examine the issues mentioned from this perspective. The Pearson correlation coefficient is used for the analysis, and the relationship between economic growth and HDI between 1980 and 2020 is examined. The selected data analysed is almost complete except for a few missing values, for example, in the case of Russia, Saudi Arabia, Canada and Iraq. However, even this fact may slightly distort the analysis. The results show a correlation for all the top producers examined. The highest correlation, very strongly positive, was observed in the case of the USA. Very strong positive correlations were also found for China, Canada, Australia, Russia, Norway, and Algeria. The lowest correlation values were recorded for Iraq, Kuwait, and the United Arab Emirates. The analysis can be used as a basis for further analysis. Keywords: Economic growth; Gas; Gross Domestic Product (GDP); Human Development Index (HDI); Oil; Pearson correlation coefficient.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".