Scientific Production Indexed in the Web of Science Basis in the Energy Management Area: A Bibliometric Analysis
Bibliographic record
Abstract
The study aimed to analyze publications in the field of energy management study. To this end, a search was carried out on the Web of Science from 1999 to 2019, searching for articles that contained in its title the keyword “Energy Manag*”. After filtering all documents, excluding those presented at conferences, 5,512 documents were obtained. We chose to analyze only documents in the “articles” mode, leaving a total of 4,610. From 2007 the growth was quite significant in the number of publications. There is a predominance of the English language. Of the top ten authors with the largest number of publications, six are linked to educational / research institutions located in China, which also ranks first in the ranking of countries with the highest number of publications. “Energies”, “Applied Energy” and “Energy” journals rank first, second and third respectively. The h-index index of publications is 111, while the h-index index of the ten best-ranked authors is 40. When compared to other studies with relative similarity, there is exponential growth in China, both number of publications, and authors linked to Chinese institutions, which, it is believed, should be the trend for the next years in this and other fields of study.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.121 | 0.183 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".