Liderazgo ético y comportamiento de los empleados. Análisis cienciométrico en la producción científica
Bibliographic record
Abstract
This scientometric study seeks to analyze scientific articles on ethical business leadership from a social-scientific perspective, considering its relationship or influence with the different behaviors of workers. Using the VOSWiever program, an analysis is carried out on 1000 articles published in Web of Science (WoS) journals from 1987 to May 2023. The results show the five main contributing countries, these being: China, the United States, England, Canada, and Pakistan and the year 2022 will be the year of greatest scientific production. There are no records of studies in Latin America; however, scientific production is found in academic sites in Venezuela, Chile, Ecuador, and Argentina. Using the Laws of Lotka, Price, and the Bradford model, the most prolific authors and the productivity of countries and magazines are discovered. Using Zipf's law and the Hirsch index, the most frequent keywords and the best-known articles are revealed. The article has sought to contribute to the eighth goal of sustainable development (SDG), that is, with the study of ethical leaders who model behaviors that favor economic growth, work well-being, and sustainability of their organizations. For future research, it is suggested that specific effects produced by this leadership as a mediator related to job performance in Latin companies be examined.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.054 | 0.062 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".