Scholarly Trends and Rankings in Mechanical Engineering and Heat Transfer: A Global Analysis of Impact and Influence
Bibliographic record
Abstract
Abstract Data generated using artificial intelligence (AI) and reported by the ScholarGPS® ranking platform are used to reveal unique scholarly trends including the impact and influence of Mechanical Engineering (ME) and Heat Transfer research. Publication and citation histories for both ME and Heat Transfer are presented for 1970–2023. A breakdown of publications is provided such as the percentage of ME publications that deal with Heat Transfer, and the percentage of Heat Transfer publications authored by ME scholars. Based on the productivity (archival publications), impact (citations), and quality (h-index) of individual scholars, the influence of countries, in both ME and Heat Transfer, is reported. Countries with growing, decreasing, and emerging influence in the last five years are identified. Top-ranked scholars and academic (and, separately, nonacademic) institutions are listed for both ME and Heat Transfer. Based on their lifetime work, the world's Top 20 Highly Ranked Scholars™ (HRS) in both ME and Heat Transfer are identified. In general, it is found that the U.S. and Canada, along with other developed nations, have suffered significant declines in their influence in both ME and Heat Transfer research. In contrast, China, India, Iran, and other developing countries have increased their scholarly influence in both ME and Heat Transfer. University rankings follow similar trends. The methodologies used to identify the preceding trends are described in detail, so that studies of any of the 14 Fields, 177 Disciplines, and 350,000 Specialties covered by ScholarGPS can be conducted by other individuals and organizations.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.046 | 0.071 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".