Perspective of ecocycles for human well-being and health: A bibliometric analysis
Bibliographic record
Abstract
The ecocycle is essential in extending the sustainable development cycle for human health and well-being. It needs to be highlighted to see what issues are behind the crossing of issues circulating so far. This research aims to explore ecocycle studies for human well-being and health during 2000-2023. A suitable bibliometric study has been conducted that visualizes the evolution and development trend of the selected studies, themes distribution, trails, keywords, and other metrics, which have been highlighted using the Biblioshiny tool derived from the R-studio package. The results found that the ecocycle for human well-being and health study has fluctuated in its evolution of publication trends, but impacts have been addressed each annual year. Prescott S.L. is a scholar who actively publishes papers, and an article by Miller K.E published in 2010 is the most cited article (n=867). The journal “International Journal of Environmental Research and Public Health” was the leading source of topics, and “University of Toronto” had the most affiliates. Furthermore, the United States served as a prolific country; trending topics such as sustainability, ecology, and mental health were among the top three. Likewise, well-being was a popular theme of research, whereby variable factors of physiology were closely coordinated. In addition, depression, inflammation, biophilosophy, and zoonosis are also featured in ecocycle studies for human well-being and health for recent publications and are expected to become future study directions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.163 | 0.278 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".