Total numbers matter. Landscape of China’s scientific publications in 2018-2020 on the energy issue
Bibliographic record
Abstract
This study aims to reveal and analyze the landscape of China’s scientific publications in 2018–2020 on the subject “Energy Engineering and Power Technology” using bibliometric data from the Lens platform. Bibliometric data of 26,623 scholarly works that satisfy the query: “Filters: Year Published = (2018–); Publication Type = (journal article); Subject = (Energy Engineering and Power Technology); Institution Country/Region = (China)” were used to analyze their main topics disclosed by Fields of Study and Subject; the leading contributors to these R&D activities were also detected. Chinese Academy of Sciences, China University of Petroleum, Tsinghua University, Xi’an Jiaotong University, China University of Mining and Technology are the leading institutions in the subject. Most research works were funded by National Natural Science Foundation of China. China carries out its research not only in conjunction with the leading economies: United States, United Kingdom, Australia and Canada, but also with the developing countries: Pakistan, Iran, Saudi Arabia and Viet Nam. Materials science, Chemical engineering, Computer science, Chemistry, Catalysis, Environmental science are the top Fields of Study. Analysis of co-occurrence of Fields of Study allowed to identify 5 thematic clusters: 1. Thermal efficiency and environmental science; 2. Materials science for energy storage and hydrogen production; 3. Catalysis and pyrolysis for better fossil fuels; 4. Computer science and control theory for renewable energy; 5. Petroleum engineering for new fossil fuel resources and composite materials. The results of the work can serve as a reference material for scientists, developers and investors, so that they can understand the research landscape of the “Energy Engineering and Power Technology” subject.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.068 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".