Green Economy and Quality of Life in the Future: Bibliometric Analysis Approach
Bibliographic record
Abstract
In reviewing this research, the author will focus on the development of research on topics used in scientific works with the theme green economy and life expectancy spread across all parts of the world over a period of 33 years starting from 1990-2023 with the Scopus database using the Scopus approach.Bibliometric Analysis.This paper aims to contribute to existing literature by answering questions, namely in the Scopus database, what are the trends in scientific history products and the amount of green economic research; what are the critical intellectual aspects and how do they influence the green economics literature; what are the future directions of green economy research.The results of the bibliometric analysis show that there is a significant increasing trend in the number of publications discussing the relationship between the green economy and life expectancy in the last thirty-three years.Overall, this bibliometric analysis confirms that the green economy has the potential to increase life expectancy through improving environmental quality.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.156 | 0.246 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".