Bibliometric Analysis of Leading Countries in Energy Research
Bibliographic record
Abstract
Given our growing dependence on energy, it is relevant to examine how to define, measure, and assess energy research and development. This study discusses the use of bibliometric methods for examining the evolution of energy research at the world level and in leading countries. The originality of the proposed method lies in the use of a several-pronged approach to delineating the field: seeding a keyword set with the output of research organisations in the field, augmenting this dataset with specialized journals, papers selected on the grounds of number of references made to a basic dataset and papers selected on the basis of citations received from papers in that basic dataset. This strategy results in both high recall and high precision. Results show that scientific output in energy research has doubled since 1996. Among leading countries, China has demonstrated a stupendous growth rate, specialization in the field, and immense scientific output. In contrast, many English-speaking countries (with the exception of Canada, which performs above the world average) are not performing as strongly, and some of the traditionally well established countries in energy R&D (e.g., the US and Japan) are progressively losing ground.
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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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.092 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".