Bibliographic record
Abstract
Climate change is a worldwide issue that can influence the way of life of all living beings. This study aims to perform a bibliometric analysis of climate crisis and climate change scientific studies. Bibliometric analyses give an in-depth assessment of the literature's publications on the subject, the identification of scientific research trends on the subject, the evaluation of researcher collaboration, and the evaluation of significant issues. The study is qualitative research, and a bibliometric research method was used. The research data was first accessed on 04 August 2022 (Time: 14:34) from the "Web of Science" database as an online search. However, some data were revised on 18 July 2023 (Time: 15:00) using the same database in order to include up-to-date data in the study. The obtained data were transferred to VOSviewer software and analyzed. According to the survey, climate-related articles most used keywords include climate change, climate crisis, sustainability, environment, climate justice, and Anthropocene. Most of the studied papers are from many disciplines, such as environmental sciences, meteorology atmospheric studies, ecology, geosciences multidisciplinary, and environmental studies. When the publications on climate catastrophe are examined by country, the most cited countries are England, Canada, United States, Sweden, and Norway. As a result, international scientific collaboration and data exchange are critical for a successful battle against climate change and the climate crisis. Collaboration and information exchange between disciplines can result in more effective and inclusive solutions. Encouraging studies in other languages and knowing common terminology can help to promote global collaboration. The examination and assessment of scientific findings are vital in enhancing societal awareness and resilience, as well as in developing long-term policy
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.191 | 0.271 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".