Evolution of the Scientific Literature on Input–Output Analysis: A Bibliometric Analysis of 1990–2017
Bibliographic record
Abstract
This study attempts to characterize the literature related to input–output analysis between 1990–2017 through bibliometric analysis technology based on the Science Citation Index and Social Sciences Citation Index databases. By means of bibliometric tools, this paper provides deep insights on the patterns of these articles, the most influential works and authors, and the emerging research topics. The results imply that China and the United States (USA) are the leading countries in terms of publication output. The Chinese Academy of Sciences is the most productive research institution, followed by Beijing Normal University and the University of Sydney. The Journal of Cleaner Production, Ecological Economics, and Energy Policy are the top mainstream journals in the input–output analysis-related field. Based on network analysis, this paper also discovers the hidden collaboration patterns and interrelations of countries, institutions, and authors. The bibliographic coupling and keywords concurrence networks are adopted to illustrate the input–output analysis evolution over time, and identify the current key research hotspots. The obtained results will help scientific researchers better understand the research status and frontier trends in this field, permit researchers to know the current research interests in the input–output analysis field, and provide useful information for further investigation and publication strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.261 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".