Mapping the Research Landscape: A Comprehensive Bibliometric Review of Global Warming and Human Health
Bibliographic record
Abstract
ABSTRACT Human health is increasingly threatened by global warming, necessitating a thorough understanding of research progress in this critical area to inform future studies. The data were analysed using Microsoft Excel 2021, and visualisations were created with GraphPad, VOSviewer and R‐Studio (Bibliometrix). These tools were used to generate graphs and network visualisations illustrating author and country collaborations, journal article distributions, hotspot clustering and future trend predictions. Our analysis revealed a substantial increase in publications in recent years, with intensified research and collaboration observed across Asia, Europe, North America and Oceania, particularly in the United States. Various high‐impact journals have made meaningful contributions to raising awareness in this field, emphasises the multifaceted impacts of global warming on public health and societal activities, while also exploring adaptive measures being implemented in response to these changes. As the world continues to grapple with the global pandemic, further research is likely to spotlight related issues, with heightened interest anticipated. The evidence of global warming's impact on human health is becoming increasingly evident, underscoring the need for global cooperation to mitigate its effects and promote public health. This study provides a foundation for researchers and policymakers, highlighting the significance of addressing global warming's implications for human well‐being. By fostering international collaboration, we can collectively strive toward sustainable strategies to combat global warming and safeguard public health.
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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 | 0.036 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.182 | 0.225 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".