Public management of scientists’ potential as a source of economic development: A bibliometric analysis
Bibliographic record
Abstract
Particular hopes have always been placed on the potential of scientists because they can act as a driving force for effective government. Understanding the importance of scientists ensured progress and prosperity for leading civilizations. This study aims to identify an evolutionary-chronological, geographical, and contextual scientific landscape of the development and management of the potential of scientists through a comprehensive bibliometric analysis. Initially, 5619 publications in the Scopus database were selected from 1957 to 2023. The evolution of knowledge about the importance of public administration of scientific personnel began in 1957 and reached its peak in 2019. Authors from the USA, Great Britain, and Australia have published more about the significance of managing the potential of scientific personnel, and strong schools of knowledge about scientific personnel are concentrated in the USA, France, Canada, and Australia. The analysis of the research’s conceptual orientation shows that publications in environmental and social sciences dominate this sphere. In addition, the bibliometric analysis results show that public management of scientific personnel will bring benefits such as effective government policy decisions, increased innovation activity, commercialization, and improvement of the population’s social life. The results of this study lay the foundation for future research that should improve the management of scientific personnel’s potential. AcknowledgmentsThis study is supported by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (AP19579256 “Mechanisms for empowering women in scientific activity in the interests of the development of the innovative economy of Kazakhstan”, 2023–2025).
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.124 | 0.195 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".